In [4]:
import pandas as pd
In [18]:
btc = pd.read_csv('coin_bitcoin.csv')btc
Out[18]:
SNo | Name | Symbol | Date | High | Low | Open | Close | Volume | Marketcap | |
0 | 1 | Bitcoin | BTC | 2013-04-29 23:59:59 | 147.488007 | 134.000000 | 134.444000 | 144.539993 | 0.000000e+00 | 1.603769e+09 |
1 | 2 | Bitcoin | BTC | 2013-04-30 23:59:59 | 146.929993 | 134.050003 | 144.000000 | 139.000000 | 0.000000e+00 | 1.542813e+09 |
2 | 3 | Bitcoin | BTC | 2013-05-01 23:59:59 | 139.889999 | 107.720001 | 139.000000 | 116.989998 | 0.000000e+00 | 1.298955e+09 |
3 | 4 | Bitcoin | BTC | 2013-05-02 23:59:59 | 125.599998 | 92.281898 | 116.379997 | 105.209999 | 0.000000e+00 | 1.168517e+09 |
4 | 5 | Bitcoin | BTC | 2013-05-03 23:59:59 | 108.127998 | 79.099998 | 106.250000 | 97.750000 | 0.000000e+00 | 1.085995e+09 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
2986 | 2987 | Bitcoin | BTC | 2021-07-02 23:59:59 | 33939.588699 | 32770.680780 | 33549.600177 | 33897.048590 | 3.872897e+10 | 6.354508e+11 |
2987 | 2988 | Bitcoin | BTC | 2021-07-03 23:59:59 | 34909.259899 | 33402.696536 | 33854.421362 | 34668.548402 | 2.438396e+10 | 6.499397e+11 |
2988 | 2989 | Bitcoin | BTC | 2021-07-04 23:59:59 | 35937.567147 | 34396.477458 | 34665.564866 | 35287.779766 | 2.492431e+10 | 6.615748e+11 |
2989 | 2990 | Bitcoin | BTC | 2021-07-05 23:59:59 | 35284.344430 | 33213.661034 | 35284.344430 | 33746.002456 | 2.672155e+10 | 6.326962e+11 |
2990 | 2991 | Bitcoin | BTC | 2021-07-06 23:59:59 | 35038.536363 | 33599.916169 | 33723.509655 | 34235.193451 | 2.650126e+10 | 6.418992e+11 |
2991 rows × 10 columns
In [4]:
btc.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2991 entries, 0 to 2990
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 SNo 2991 non-null int64
1 Name 2991 non-null object
2 Symbol 2991 non-null object
3 Date 2991 non-null object
4 High 2991 non-null float64
5 Low 2991 non-null float64
6 Open 2991 non-null float64
7 Close 2991 non-null float64
8 Volume 2991 non-null float64
9 Marketcap 2991 non-null float64
dtypes: float64(6), int64(1), object(3)
memory usage: 233.8+ KB
In [28]:
btc.set_index('High', inplace=False)
Out[28]:
SNo | Name | Symbol | Low | Open | Close | Volume | Marketcap | |
High | ||||||||
147.488007 | 1 | Bitcoin | BTC | 134.000000 | 134.444000 | 144.539993 | 0.000000e+00 | 1.603769e+09 |
146.929993 | 2 | Bitcoin | BTC | 134.050003 | 144.000000 | 139.000000 | 0.000000e+00 | 1.542813e+09 |
139.889999 | 3 | Bitcoin | BTC | 107.720001 | 139.000000 | 116.989998 | 0.000000e+00 | 1.298955e+09 |
125.599998 | 4 | Bitcoin | BTC | 92.281898 | 116.379997 | 105.209999 | 0.000000e+00 | 1.168517e+09 |
108.127998 | 5 | Bitcoin | BTC | 79.099998 | 106.250000 | 97.750000 | 0.000000e+00 | 1.085995e+09 |
... | ... | ... | ... | ... | ... | ... | ... | ... |
33939.588699 | 2987 | Bitcoin | BTC | 32770.680780 | 33549.600177 | 33897.048590 | 3.872897e+10 | 6.354508e+11 |
34909.259899 | 2988 | Bitcoin | BTC | 33402.696536 | 33854.421362 | 34668.548402 | 2.438396e+10 | 6.499397e+11 |
35937.567147 | 2989 | Bitcoin | BTC | 34396.477458 | 34665.564866 | 35287.779766 | 2.492431e+10 | 6.615748e+11 |
35284.344430 | 2990 | Bitcoin | BTC | 33213.661034 | 35284.344430 | 33746.002456 | 2.672155e+10 | 6.326962e+11 |
35038.536363 | 2991 | Bitcoin | BTC | 33599.916169 | 33723.509655 | 34235.193451 | 2.650126e+10 | 6.418992e+11 |
2991 rows × 8 columns
In [29]:
btc.Open
Out[29]:
Date
2013-04-29 23:59:59 134.444000
2013-04-30 23:59:59 144.000000
2013-05-01 23:59:59 139.000000
2013-05-02 23:59:59 116.379997
2013-05-03 23:59:59 106.250000
...
2021-07-02 23:59:59 33549.600177
2021-07-03 23:59:59 33854.421362
2021-07-04 23:59:59 34665.564866
2021-07-05 23:59:59 35284.344430
2021-07-06 23:59:59 33723.509655
Name: Open, Length: 2991, dtype: float64
In [30]:
btc.High.plot()
Out[30]:
<Axes: xlabel='Date'>

In [32]:
btc = pd.read_csv('coin_bitcoin.csv')btc.High
Out[32]:
0 147.488007
1 146.929993
2 139.889999
3 125.599998
4 108.127998
...
2986 33939.588699
2987 34909.259899
2988 35937.567147
2989 35284.344430
2990 35038.536363
Name: High, Length: 2991, dtype: float64
In [34]:
btc.High.plot()
Out[34]:
<Axes: >

In [13]:
countries = pd.read_csv('world-happiness-report-2021.csv')countries
Out[13]:
Country name | Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
0 | Finland | Western Europe | 7.842 | 0.032 | 7.904 | 7.780 | 10.775 | 0.954 | 72.000 | 0.949 | -0.098 | 0.186 | 2.43 | 1.446 | 1.106 | 0.741 | 0.691 | 0.124 | 0.481 | 3.253 |
1 | Denmark | Western Europe | 7.620 | 0.035 | 7.687 | 7.552 | 10.933 | 0.954 | 72.700 | 0.946 | 0.030 | 0.179 | 2.43 | 1.502 | 1.108 | 0.763 | 0.686 | 0.208 | 0.485 | 2.868 |
2 | Switzerland | Western Europe | 7.571 | 0.036 | 7.643 | 7.500 | 11.117 | 0.942 | 74.400 | 0.919 | 0.025 | 0.292 | 2.43 | 1.566 | 1.079 | 0.816 | 0.653 | 0.204 | 0.413 | 2.839 |
3 | Iceland | Western Europe | 7.554 | 0.059 | 7.670 | 7.438 | 10.878 | 0.983 | 73.000 | 0.955 | 0.160 | 0.673 | 2.43 | 1.482 | 1.172 | 0.772 | 0.698 | 0.293 | 0.170 | 2.967 |
4 | Netherlands | Western Europe | 7.464 | 0.027 | 7.518 | 7.410 | 10.932 | 0.942 | 72.400 | 0.913 | 0.175 | 0.338 | 2.43 | 1.501 | 1.079 | 0.753 | 0.647 | 0.302 | 0.384 | 2.798 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
144 | Lesotho | Sub-Saharan Africa | 3.512 | 0.120 | 3.748 | 3.276 | 7.926 | 0.787 | 48.700 | 0.715 | -0.131 | 0.915 | 2.43 | 0.451 | 0.731 | 0.007 | 0.405 | 0.103 | 0.015 | 1.800 |
145 | Botswana | Sub-Saharan Africa | 3.467 | 0.074 | 3.611 | 3.322 | 9.782 | 0.784 | 59.269 | 0.824 | -0.246 | 0.801 | 2.43 | 1.099 | 0.724 | 0.340 | 0.539 | 0.027 | 0.088 | 0.648 |
146 | Rwanda | Sub-Saharan Africa | 3.415 | 0.068 | 3.548 | 3.282 | 7.676 | 0.552 | 61.400 | 0.897 | 0.061 | 0.167 | 2.43 | 0.364 | 0.202 | 0.407 | 0.627 | 0.227 | 0.493 | 1.095 |
147 | Zimbabwe | Sub-Saharan Africa | 3.145 | 0.058 | 3.259 | 3.030 | 7.943 | 0.750 | 56.201 | 0.677 | -0.047 | 0.821 | 2.43 | 0.457 | 0.649 | 0.243 | 0.359 | 0.157 | 0.075 | 1.205 |
148 | Afghanistan | South Asia | 2.523 | 0.038 | 2.596 | 2.449 | 7.695 | 0.463 | 52.493 | 0.382 | -0.102 | 0.924 | 2.43 | 0.370 | 0.000 | 0.126 | 0.000 | 0.122 | 0.010 | 1.895 |
149 rows × 20 columns
In [230]:
countries[['Healthy life expectancy']]
Out[230]:
Healthy life expectancy | |
0 | 72.000 |
1 | 72.700 |
2 | 74.400 |
3 | 73.000 |
4 | 72.400 |
... | ... |
144 | 48.700 |
145 | 59.269 |
146 | 61.400 |
147 | 56.201 |
148 | 52.493 |
149 rows × 1 columns
In [15]:
countries.set_index('Country name')
Out[15]:
Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
Country name | |||||||||||||||||||
Finland | Western Europe | 7.842 | 0.032 | 7.904 | 7.780 | 10.775 | 0.954 | 72.000 | 0.949 | -0.098 | 0.186 | 2.43 | 1.446 | 1.106 | 0.741 | 0.691 | 0.124 | 0.481 | 3.253 |
Denmark | Western Europe | 7.620 | 0.035 | 7.687 | 7.552 | 10.933 | 0.954 | 72.700 | 0.946 | 0.030 | 0.179 | 2.43 | 1.502 | 1.108 | 0.763 | 0.686 | 0.208 | 0.485 | 2.868 |
Switzerland | Western Europe | 7.571 | 0.036 | 7.643 | 7.500 | 11.117 | 0.942 | 74.400 | 0.919 | 0.025 | 0.292 | 2.43 | 1.566 | 1.079 | 0.816 | 0.653 | 0.204 | 0.413 | 2.839 |
Iceland | Western Europe | 7.554 | 0.059 | 7.670 | 7.438 | 10.878 | 0.983 | 73.000 | 0.955 | 0.160 | 0.673 | 2.43 | 1.482 | 1.172 | 0.772 | 0.698 | 0.293 | 0.170 | 2.967 |
Netherlands | Western Europe | 7.464 | 0.027 | 7.518 | 7.410 | 10.932 | 0.942 | 72.400 | 0.913 | 0.175 | 0.338 | 2.43 | 1.501 | 1.079 | 0.753 | 0.647 | 0.302 | 0.384 | 2.798 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
Lesotho | Sub-Saharan Africa | 3.512 | 0.120 | 3.748 | 3.276 | 7.926 | 0.787 | 48.700 | 0.715 | -0.131 | 0.915 | 2.43 | 0.451 | 0.731 | 0.007 | 0.405 | 0.103 | 0.015 | 1.800 |
Botswana | Sub-Saharan Africa | 3.467 | 0.074 | 3.611 | 3.322 | 9.782 | 0.784 | 59.269 | 0.824 | -0.246 | 0.801 | 2.43 | 1.099 | 0.724 | 0.340 | 0.539 | 0.027 | 0.088 | 0.648 |
Rwanda | Sub-Saharan Africa | 3.415 | 0.068 | 3.548 | 3.282 | 7.676 | 0.552 | 61.400 | 0.897 | 0.061 | 0.167 | 2.43 | 0.364 | 0.202 | 0.407 | 0.627 | 0.227 | 0.493 | 1.095 |
Zimbabwe | Sub-Saharan Africa | 3.145 | 0.058 | 3.259 | 3.030 | 7.943 | 0.750 | 56.201 | 0.677 | -0.047 | 0.821 | 2.43 | 0.457 | 0.649 | 0.243 | 0.359 | 0.157 | 0.075 | 1.205 |
Afghanistan | South Asia | 2.523 | 0.038 | 2.596 | 2.449 | 7.695 | 0.463 | 52.493 | 0.382 | -0.102 | 0.924 | 2.43 | 0.370 | 0.000 | 0.126 | 0.000 | 0.122 | 0.010 | 1.895 |
149 rows × 19 columns
In [24]:
countries['Healthy life expectancy'].head(25).plot(kind = 'barh', color = 'magenta')
Out[24]:
<Axes: >

In [28]:
countries = pd.read_csv('world-happiness-report-2021.csv', index_col = 'Country name')countries
Out[28]:
Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
Country name | |||||||||||||||||||
Finland | Western Europe | 7.842 | 0.032 | 7.904 | 7.780 | 10.775 | 0.954 | 72.000 | 0.949 | -0.098 | 0.186 | 2.43 | 1.446 | 1.106 | 0.741 | 0.691 | 0.124 | 0.481 | 3.253 |
Denmark | Western Europe | 7.620 | 0.035 | 7.687 | 7.552 | 10.933 | 0.954 | 72.700 | 0.946 | 0.030 | 0.179 | 2.43 | 1.502 | 1.108 | 0.763 | 0.686 | 0.208 | 0.485 | 2.868 |
Switzerland | Western Europe | 7.571 | 0.036 | 7.643 | 7.500 | 11.117 | 0.942 | 74.400 | 0.919 | 0.025 | 0.292 | 2.43 | 1.566 | 1.079 | 0.816 | 0.653 | 0.204 | 0.413 | 2.839 |
Iceland | Western Europe | 7.554 | 0.059 | 7.670 | 7.438 | 10.878 | 0.983 | 73.000 | 0.955 | 0.160 | 0.673 | 2.43 | 1.482 | 1.172 | 0.772 | 0.698 | 0.293 | 0.170 | 2.967 |
Netherlands | Western Europe | 7.464 | 0.027 | 7.518 | 7.410 | 10.932 | 0.942 | 72.400 | 0.913 | 0.175 | 0.338 | 2.43 | 1.501 | 1.079 | 0.753 | 0.647 | 0.302 | 0.384 | 2.798 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
Lesotho | Sub-Saharan Africa | 3.512 | 0.120 | 3.748 | 3.276 | 7.926 | 0.787 | 48.700 | 0.715 | -0.131 | 0.915 | 2.43 | 0.451 | 0.731 | 0.007 | 0.405 | 0.103 | 0.015 | 1.800 |
Botswana | Sub-Saharan Africa | 3.467 | 0.074 | 3.611 | 3.322 | 9.782 | 0.784 | 59.269 | 0.824 | -0.246 | 0.801 | 2.43 | 1.099 | 0.724 | 0.340 | 0.539 | 0.027 | 0.088 | 0.648 |
Rwanda | Sub-Saharan Africa | 3.415 | 0.068 | 3.548 | 3.282 | 7.676 | 0.552 | 61.400 | 0.897 | 0.061 | 0.167 | 2.43 | 0.364 | 0.202 | 0.407 | 0.627 | 0.227 | 0.493 | 1.095 |
Zimbabwe | Sub-Saharan Africa | 3.145 | 0.058 | 3.259 | 3.030 | 7.943 | 0.750 | 56.201 | 0.677 | -0.047 | 0.821 | 2.43 | 0.457 | 0.649 | 0.243 | 0.359 | 0.157 | 0.075 | 1.205 |
Afghanistan | South Asia | 2.523 | 0.038 | 2.596 | 2.449 | 7.695 | 0.463 | 52.493 | 0.382 | -0.102 | 0.924 | 2.43 | 0.370 | 0.000 | 0.126 | 0.000 | 0.122 | 0.010 | 1.895 |
149 rows × 19 columns
In [32]:
countries = pd.read_csv('world-happiness-report-2021.csv')countries
Out[32]:
Country name | Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
0 | Finland | Western Europe | 7.842 | 0.032 | 7.904 | 7.780 | 10.775 | 0.954 | 72.000 | 0.949 | -0.098 | 0.186 | 2.43 | 1.446 | 1.106 | 0.741 | 0.691 | 0.124 | 0.481 | 3.253 |
1 | Denmark | Western Europe | 7.620 | 0.035 | 7.687 | 7.552 | 10.933 | 0.954 | 72.700 | 0.946 | 0.030 | 0.179 | 2.43 | 1.502 | 1.108 | 0.763 | 0.686 | 0.208 | 0.485 | 2.868 |
2 | Switzerland | Western Europe | 7.571 | 0.036 | 7.643 | 7.500 | 11.117 | 0.942 | 74.400 | 0.919 | 0.025 | 0.292 | 2.43 | 1.566 | 1.079 | 0.816 | 0.653 | 0.204 | 0.413 | 2.839 |
3 | Iceland | Western Europe | 7.554 | 0.059 | 7.670 | 7.438 | 10.878 | 0.983 | 73.000 | 0.955 | 0.160 | 0.673 | 2.43 | 1.482 | 1.172 | 0.772 | 0.698 | 0.293 | 0.170 | 2.967 |
4 | Netherlands | Western Europe | 7.464 | 0.027 | 7.518 | 7.410 | 10.932 | 0.942 | 72.400 | 0.913 | 0.175 | 0.338 | 2.43 | 1.501 | 1.079 | 0.753 | 0.647 | 0.302 | 0.384 | 2.798 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
144 | Lesotho | Sub-Saharan Africa | 3.512 | 0.120 | 3.748 | 3.276 | 7.926 | 0.787 | 48.700 | 0.715 | -0.131 | 0.915 | 2.43 | 0.451 | 0.731 | 0.007 | 0.405 | 0.103 | 0.015 | 1.800 |
145 | Botswana | Sub-Saharan Africa | 3.467 | 0.074 | 3.611 | 3.322 | 9.782 | 0.784 | 59.269 | 0.824 | -0.246 | 0.801 | 2.43 | 1.099 | 0.724 | 0.340 | 0.539 | 0.027 | 0.088 | 0.648 |
146 | Rwanda | Sub-Saharan Africa | 3.415 | 0.068 | 3.548 | 3.282 | 7.676 | 0.552 | 61.400 | 0.897 | 0.061 | 0.167 | 2.43 | 0.364 | 0.202 | 0.407 | 0.627 | 0.227 | 0.493 | 1.095 |
147 | Zimbabwe | Sub-Saharan Africa | 3.145 | 0.058 | 3.259 | 3.030 | 7.943 | 0.750 | 56.201 | 0.677 | -0.047 | 0.821 | 2.43 | 0.457 | 0.649 | 0.243 | 0.359 | 0.157 | 0.075 | 1.205 |
148 | Afghanistan | South Asia | 2.523 | 0.038 | 2.596 | 2.449 | 7.695 | 0.463 | 52.493 | 0.382 | -0.102 | 0.924 | 2.43 | 0.370 | 0.000 | 0.126 | 0.000 | 0.122 | 0.010 | 1.895 |
149 rows × 20 columns
In [35]:
countries.sort_values('Healthy life expectancy')
Out[35]:
Country name | Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
127 | Chad | Sub-Saharan Africa | 4.355 | 0.094 | 4.540 | 4.171 | 7.364 | 0.619 | 48.478 | 0.579 | 0.041 | 0.807 | 2.43 | 0.255 | 0.353 | 0.000 | 0.240 | 0.215 | 0.084 | 3.209 |
144 | Lesotho | Sub-Saharan Africa | 3.512 | 0.120 | 3.748 | 3.276 | 7.926 | 0.787 | 48.700 | 0.715 | -0.131 | 0.915 | 2.43 | 0.451 | 0.731 | 0.007 | 0.405 | 0.103 | 0.015 | 1.800 |
115 | Nigeria | Sub-Saharan Africa | 4.759 | 0.052 | 4.861 | 4.658 | 8.533 | 0.740 | 50.102 | 0.737 | 0.037 | 0.878 | 2.43 | 0.663 | 0.625 | 0.051 | 0.433 | 0.212 | 0.039 | 2.736 |
84 | Ivory Coast | Sub-Saharan Africa | 5.306 | 0.078 | 5.460 | 5.152 | 8.551 | 0.644 | 50.114 | 0.741 | -0.016 | 0.794 | 2.43 | 0.669 | 0.409 | 0.052 | 0.438 | 0.177 | 0.092 | 3.469 |
129 | Swaziland | Sub-Saharan Africa | 4.308 | 0.071 | 4.448 | 4.168 | 9.065 | 0.770 | 50.833 | 0.647 | -0.185 | 0.708 | 2.43 | 0.849 | 0.693 | 0.074 | 0.323 | 0.067 | 0.147 | 2.155 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
2 | Switzerland | Western Europe | 7.571 | 0.036 | 7.643 | 7.500 | 11.117 | 0.942 | 74.400 | 0.919 | 0.025 | 0.292 | 2.43 | 1.566 | 1.079 | 0.816 | 0.653 | 0.204 | 0.413 | 2.839 |
26 | Spain | Western Europe | 6.491 | 0.042 | 6.574 | 6.408 | 10.571 | 0.932 | 74.700 | 0.761 | -0.081 | 0.745 | 2.43 | 1.375 | 1.057 | 0.826 | 0.462 | 0.135 | 0.124 | 2.513 |
55 | Japan | East Asia | 5.940 | 0.040 | 6.020 | 5.861 | 10.611 | 0.884 | 75.100 | 0.796 | -0.258 | 0.638 | 2.43 | 1.389 | 0.949 | 0.838 | 0.504 | 0.020 | 0.192 | 2.048 |
76 | Hong Kong S.A.R. of China | East Asia | 5.477 | 0.049 | 5.573 | 5.380 | 11.000 | 0.836 | 76.820 | 0.717 | 0.067 | 0.403 | 2.43 | 1.525 | 0.841 | 0.893 | 0.408 | 0.232 | 0.342 | 1.236 |
31 | Singapore | Southeast Asia | 6.377 | 0.043 | 6.460 | 6.293 | 11.488 | 0.915 | 76.953 | 0.927 | -0.018 | 0.082 | 2.43 | 1.695 | 1.019 | 0.897 | 0.664 | 0.176 | 0.547 | 1.379 |
149 rows × 20 columns
In [36]:
countries.sort_values('Healthy life expectancy',ascending = False)
Out[36]:
Country name | Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
31 | Singapore | Southeast Asia | 6.377 | 0.043 | 6.460 | 6.293 | 11.488 | 0.915 | 76.953 | 0.927 | -0.018 | 0.082 | 2.43 | 1.695 | 1.019 | 0.897 | 0.664 | 0.176 | 0.547 | 1.379 |
76 | Hong Kong S.A.R. of China | East Asia | 5.477 | 0.049 | 5.573 | 5.380 | 11.000 | 0.836 | 76.820 | 0.717 | 0.067 | 0.403 | 2.43 | 1.525 | 0.841 | 0.893 | 0.408 | 0.232 | 0.342 | 1.236 |
55 | Japan | East Asia | 5.940 | 0.040 | 6.020 | 5.861 | 10.611 | 0.884 | 75.100 | 0.796 | -0.258 | 0.638 | 2.43 | 1.389 | 0.949 | 0.838 | 0.504 | 0.020 | 0.192 | 2.048 |
26 | Spain | Western Europe | 6.491 | 0.042 | 6.574 | 6.408 | 10.571 | 0.932 | 74.700 | 0.761 | -0.081 | 0.745 | 2.43 | 1.375 | 1.057 | 0.826 | 0.462 | 0.135 | 0.124 | 2.513 |
2 | Switzerland | Western Europe | 7.571 | 0.036 | 7.643 | 7.500 | 11.117 | 0.942 | 74.400 | 0.919 | 0.025 | 0.292 | 2.43 | 1.566 | 1.079 | 0.816 | 0.653 | 0.204 | 0.413 | 2.839 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
129 | Swaziland | Sub-Saharan Africa | 4.308 | 0.071 | 4.448 | 4.168 | 9.065 | 0.770 | 50.833 | 0.647 | -0.185 | 0.708 | 2.43 | 0.849 | 0.693 | 0.074 | 0.323 | 0.067 | 0.147 | 2.155 |
84 | Ivory Coast | Sub-Saharan Africa | 5.306 | 0.078 | 5.460 | 5.152 | 8.551 | 0.644 | 50.114 | 0.741 | -0.016 | 0.794 | 2.43 | 0.669 | 0.409 | 0.052 | 0.438 | 0.177 | 0.092 | 3.469 |
115 | Nigeria | Sub-Saharan Africa | 4.759 | 0.052 | 4.861 | 4.658 | 8.533 | 0.740 | 50.102 | 0.737 | 0.037 | 0.878 | 2.43 | 0.663 | 0.625 | 0.051 | 0.433 | 0.212 | 0.039 | 2.736 |
144 | Lesotho | Sub-Saharan Africa | 3.512 | 0.120 | 3.748 | 3.276 | 7.926 | 0.787 | 48.700 | 0.715 | -0.131 | 0.915 | 2.43 | 0.451 | 0.731 | 0.007 | 0.405 | 0.103 | 0.015 | 1.800 |
127 | Chad | Sub-Saharan Africa | 4.355 | 0.094 | 4.540 | 4.171 | 7.364 | 0.619 | 48.478 | 0.579 | 0.041 | 0.807 | 2.43 | 0.255 | 0.353 | 0.000 | 0.240 | 0.215 | 0.084 | 3.209 |
149 rows × 20 columns
In [38]:
houses = pd.read_csv('kc_house_data.csv')titanic = pd.read_csv('titanic.csv')
In [41]:
houses.sort_values(['price','bedrooms','bathrooms'], ascending = False)
Out[41]:
id | date | price | bedrooms | bathrooms | sqft_living | sqft_lot | floors | waterfront | view | ... | grade | sqft_above | sqft_basement | yr_built | yr_renovated | zipcode | lat | long | sqft_living15 | sqft_lot15 | |
7252 | 6762700020 | 20141013T000000 | 7700000.0 | 6 | 8.00 | 12050 | 27600 | 2.5 | 0 | 3 | ... | 13 | 8570 | 3480 | 1910 | 1987 | 98102 | 47.6298 | -122.323 | 3940 | 8800 |
3914 | 9808700762 | 20140611T000000 | 7062500.0 | 5 | 4.50 | 10040 | 37325 | 2.0 | 1 | 2 | ... | 11 | 7680 | 2360 | 1940 | 2001 | 98004 | 47.6500 | -122.214 | 3930 | 25449 |
9254 | 9208900037 | 20140919T000000 | 6885000.0 | 6 | 7.75 | 9890 | 31374 | 2.0 | 0 | 4 | ... | 13 | 8860 | 1030 | 2001 | 0 | 98039 | 47.6305 | -122.240 | 4540 | 42730 |
4411 | 2470100110 | 20140804T000000 | 5570000.0 | 5 | 5.75 | 9200 | 35069 | 2.0 | 0 | 0 | ... | 13 | 6200 | 3000 | 2001 | 0 | 98039 | 47.6289 | -122.233 | 3560 | 24345 |
1448 | 8907500070 | 20150413T000000 | 5350000.0 | 5 | 5.00 | 8000 | 23985 | 2.0 | 0 | 4 | ... | 12 | 6720 | 1280 | 2009 | 0 | 98004 | 47.6232 | -122.220 | 4600 | 21750 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
8274 | 3883800011 | 20141105T000000 | 82000.0 | 3 | 1.00 | 860 | 10426 | 1.0 | 0 | 0 | ... | 6 | 860 | 0 | 1954 | 0 | 98146 | 47.4987 | -122.341 | 1140 | 11250 |
16198 | 3028200080 | 20150324T000000 | 81000.0 | 2 | 1.00 | 730 | 9975 | 1.0 | 0 | 0 | ... | 5 | 730 | 0 | 1943 | 0 | 98168 | 47.4808 | -122.315 | 860 | 9000 |
465 | 8658300340 | 20140523T000000 | 80000.0 | 1 | 0.75 | 430 | 5050 | 1.0 | 0 | 0 | ... | 4 | 430 | 0 | 1912 | 0 | 98014 | 47.6499 | -121.909 | 1200 | 7500 |
15293 | 40000362 | 20140506T000000 | 78000.0 | 2 | 1.00 | 780 | 16344 | 1.0 | 0 | 0 | ... | 5 | 780 | 0 | 1942 | 0 | 98168 | 47.4739 | -122.280 | 1700 | 10387 |
1149 | 3421079032 | 20150217T000000 | 75000.0 | 1 | 0.00 | 670 | 43377 | 1.0 | 0 | 0 | ... | 3 | 670 | 0 | 1966 | 0 | 98022 | 47.2638 | -121.906 | 1160 | 42882 |
21613 rows × 21 columns
In [42]:
titanic.head(10)
Out[42]:
pclass | survived | name | sex | age | sibsp | parch | ticket | fare | cabin | embarked | boat | body | home.dest | |
0 | 1 | 1 | Allen, Miss. Elisabeth Walton | female | 29 | 0 | 0 | 24160 | 211.3375 | B5 | S | 2 | ? | St Louis, MO |
1 | 1 | 1 | Allison, Master. Hudson Trevor | male | 0.9167 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | 11 | ? | Montreal, PQ / Chesterville, ON |
2 | 1 | 0 | Allison, Miss. Helen Loraine | female | 2 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | ? | ? | Montreal, PQ / Chesterville, ON |
3 | 1 | 0 | Allison, Mr. Hudson Joshua Creighton | male | 30 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | ? | 135 | Montreal, PQ / Chesterville, ON |
4 | 1 | 0 | Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | female | 25 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | ? | ? | Montreal, PQ / Chesterville, ON |
5 | 1 | 1 | Anderson, Mr. Harry | male | 48 | 0 | 0 | 19952 | 26.55 | E12 | S | 3 | ? | New York, NY |
6 | 1 | 1 | Andrews, Miss. Kornelia Theodosia | female | 63 | 1 | 0 | 13502 | 77.9583 | D7 | S | 10 | ? | Hudson, NY |
7 | 1 | 0 | Andrews, Mr. Thomas Jr | male | 39 | 0 | 0 | 112050 | 0 | A36 | S | ? | ? | Belfast, NI |
8 | 1 | 1 | Appleton, Mrs. Edward Dale (Charlotte Lamson) | female | 53 | 2 | 0 | 11769 | 51.4792 | C101 | S | D | ? | Bayside, Queens, NY |
9 | 1 | 0 | Artagaveytia, Mr. Ramon | male | 71 | 0 | 0 | PC 17609 | 49.5042 | ? | C | ? | 22 | Montevideo, Uruguay |
In [44]:
titanic.sort_values('name', key = lambda col: col.str.lower()).head(20)
Out[44]:
pclass | survived | name | sex | age | sibsp | parch | ticket | fare | cabin | embarked | boat | body | home.dest | |
600 | 3 | 0 | Abbing, Mr. Anthony | male | 42 | 0 | 0 | C.A. 5547 | 7.55 | ? | S | ? | ? | ? |
601 | 3 | 0 | Abbott, Master. Eugene Joseph | male | 13 | 0 | 2 | C.A. 2673 | 20.25 | ? | S | ? | ? | East Providence, RI |
602 | 3 | 0 | Abbott, Mr. Rossmore Edward | male | 16 | 1 | 1 | C.A. 2673 | 20.25 | ? | S | ? | 190 | East Providence, RI |
603 | 3 | 1 | Abbott, Mrs. Stanton (Rosa Hunt) | female | 35 | 1 | 1 | C.A. 2673 | 20.25 | ? | S | A | ? | East Providence, RI |
604 | 3 | 1 | Abelseth, Miss. Karen Marie | female | 16 | 0 | 0 | 348125 | 7.65 | ? | S | 16 | ? | Norway Los Angeles, CA |
605 | 3 | 1 | Abelseth, Mr. Olaus Jorgensen | male | 25 | 0 | 0 | 348122 | 7.65 | F G63 | S | A | ? | Perkins County, SD |
323 | 2 | 0 | Abelson, Mr. Samuel | male | 30 | 1 | 0 | P/PP 3381 | 24 | ? | C | ? | ? | Russia New York, NY |
324 | 2 | 1 | Abelson, Mrs. Samuel (Hannah Wizosky) | female | 28 | 1 | 0 | P/PP 3381 | 24 | ? | C | 10 | ? | Russia New York, NY |
606 | 3 | 1 | Abrahamsson, Mr. Abraham August Johannes | male | 20 | 0 | 0 | SOTON/O2 3101284 | 7.925 | ? | S | 15 | ? | Taalintehdas, Finland Hoboken, NJ |
607 | 3 | 1 | Abrahim, Mrs. Joseph (Sophie Halaut Easu) | female | 18 | 0 | 0 | 2657 | 7.2292 | ? | C | C | ? | Greensburg, PA |
608 | 3 | 0 | Adahl, Mr. Mauritz Nils Martin | male | 30 | 0 | 0 | C 7076 | 7.25 | ? | S | ? | 72 | Asarum, Sweden Brooklyn, NY |
609 | 3 | 0 | Adams, Mr. John | male | 26 | 0 | 0 | 341826 | 8.05 | ? | S | ? | 103 | Bournemouth, England |
610 | 3 | 0 | Ahlin, Mrs. Johan (Johanna Persdotter Larsson) | female | 40 | 1 | 0 | 7546 | 9.475 | ? | S | ? | ? | Sweden Akeley, MN |
611 | 3 | 1 | Aks, Master. Philip Frank | male | 0.8333 | 0 | 1 | 392091 | 9.35 | ? | S | 11 | ? | London, England Norfolk, VA |
612 | 3 | 1 | Aks, Mrs. Sam (Leah Rosen) | female | 18 | 0 | 1 | 392091 | 9.35 | ? | S | 13 | ? | London, England Norfolk, VA |
613 | 3 | 1 | Albimona, Mr. Nassef Cassem | male | 26 | 0 | 0 | 2699 | 18.7875 | ? | C | 15 | ? | Syria Fredericksburg, VA |
325 | 2 | 0 | Aldworth, Mr. Charles Augustus | male | 30 | 0 | 0 | 248744 | 13 | ? | S | ? | ? | Bryn Mawr, PA, USA |
614 | 3 | 0 | Alexander, Mr. William | male | 26 | 0 | 0 | 3474 | 7.8875 | ? | S | ? | ? | England Albion, NY |
615 | 3 | 0 | Alhomaki, Mr. Ilmari Rudolf | male | 20 | 0 | 0 | SOTON/O2 3101287 | 7.925 | ? | S | ? | ? | Salo, Finland Astoria, OR |
616 | 3 | 0 | Ali, Mr. Ahmed | male | 24 | 0 | 0 | SOTON/O.Q. 3101311 | 7.05 | ? | S | ? | ? | ? |
In [45]:
countries
Out[45]:
Country name | Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
0 | Finland | Western Europe | 7.842 | 0.032 | 7.904 | 7.780 | 10.775 | 0.954 | 72.000 | 0.949 | -0.098 | 0.186 | 2.43 | 1.446 | 1.106 | 0.741 | 0.691 | 0.124 | 0.481 | 3.253 |
1 | Denmark | Western Europe | 7.620 | 0.035 | 7.687 | 7.552 | 10.933 | 0.954 | 72.700 | 0.946 | 0.030 | 0.179 | 2.43 | 1.502 | 1.108 | 0.763 | 0.686 | 0.208 | 0.485 | 2.868 |
2 | Switzerland | Western Europe | 7.571 | 0.036 | 7.643 | 7.500 | 11.117 | 0.942 | 74.400 | 0.919 | 0.025 | 0.292 | 2.43 | 1.566 | 1.079 | 0.816 | 0.653 | 0.204 | 0.413 | 2.839 |
3 | Iceland | Western Europe | 7.554 | 0.059 | 7.670 | 7.438 | 10.878 | 0.983 | 73.000 | 0.955 | 0.160 | 0.673 | 2.43 | 1.482 | 1.172 | 0.772 | 0.698 | 0.293 | 0.170 | 2.967 |
4 | Netherlands | Western Europe | 7.464 | 0.027 | 7.518 | 7.410 | 10.932 | 0.942 | 72.400 | 0.913 | 0.175 | 0.338 | 2.43 | 1.501 | 1.079 | 0.753 | 0.647 | 0.302 | 0.384 | 2.798 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
144 | Lesotho | Sub-Saharan Africa | 3.512 | 0.120 | 3.748 | 3.276 | 7.926 | 0.787 | 48.700 | 0.715 | -0.131 | 0.915 | 2.43 | 0.451 | 0.731 | 0.007 | 0.405 | 0.103 | 0.015 | 1.800 |
145 | Botswana | Sub-Saharan Africa | 3.467 | 0.074 | 3.611 | 3.322 | 9.782 | 0.784 | 59.269 | 0.824 | -0.246 | 0.801 | 2.43 | 1.099 | 0.724 | 0.340 | 0.539 | 0.027 | 0.088 | 0.648 |
146 | Rwanda | Sub-Saharan Africa | 3.415 | 0.068 | 3.548 | 3.282 | 7.676 | 0.552 | 61.400 | 0.897 | 0.061 | 0.167 | 2.43 | 0.364 | 0.202 | 0.407 | 0.627 | 0.227 | 0.493 | 1.095 |
147 | Zimbabwe | Sub-Saharan Africa | 3.145 | 0.058 | 3.259 | 3.030 | 7.943 | 0.750 | 56.201 | 0.677 | -0.047 | 0.821 | 2.43 | 0.457 | 0.649 | 0.243 | 0.359 | 0.157 | 0.075 | 1.205 |
148 | Afghanistan | South Asia | 2.523 | 0.038 | 2.596 | 2.449 | 7.695 | 0.463 | 52.493 | 0.382 | -0.102 | 0.924 | 2.43 | 0.370 | 0.000 | 0.126 | 0.000 | 0.122 | 0.010 | 1.895 |
149 rows × 20 columns
In [49]:
countries.set_index('Country name').sort_index(ascending = False)
Out[49]:
Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
Country name | |||||||||||||||||||
Zimbabwe | Sub-Saharan Africa | 3.145 | 0.058 | 3.259 | 3.030 | 7.943 | 0.750 | 56.201 | 0.677 | -0.047 | 0.821 | 2.43 | 0.457 | 0.649 | 0.243 | 0.359 | 0.157 | 0.075 | 1.205 |
Zambia | Sub-Saharan Africa | 4.073 | 0.069 | 4.209 | 3.938 | 8.145 | 0.708 | 55.809 | 0.782 | 0.061 | 0.823 | 2.43 | 0.528 | 0.552 | 0.231 | 0.487 | 0.227 | 0.074 | 1.975 |
Yemen | Middle East and North Africa | 3.658 | 0.070 | 3.794 | 3.521 | 7.578 | 0.832 | 57.122 | 0.602 | -0.147 | 0.800 | 2.43 | 0.329 | 0.831 | 0.272 | 0.268 | 0.092 | 0.089 | 1.776 |
Vietnam | Southeast Asia | 5.411 | 0.039 | 5.488 | 5.334 | 8.973 | 0.850 | 68.034 | 0.940 | -0.098 | 0.796 | 2.43 | 0.817 | 0.873 | 0.616 | 0.679 | 0.124 | 0.091 | 2.211 |
Venezuela | Latin America and Caribbean | 4.892 | 0.064 | 5.017 | 4.767 | 9.073 | 0.861 | 66.700 | 0.615 | -0.169 | 0.827 | 2.43 | 0.852 | 0.897 | 0.574 | 0.284 | 0.078 | 0.072 | 2.135 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
Armenia | Commonwealth of Independent States | 5.283 | 0.058 | 5.397 | 5.168 | 9.487 | 0.799 | 67.055 | 0.825 | -0.168 | 0.629 | 2.43 | 0.996 | 0.758 | 0.585 | 0.540 | 0.079 | 0.198 | 2.127 |
Argentina | Latin America and Caribbean | 5.929 | 0.056 | 6.040 | 5.819 | 9.962 | 0.898 | 69.000 | 0.828 | -0.182 | 0.834 | 2.43 | 1.162 | 0.980 | 0.646 | 0.544 | 0.069 | 0.067 | 2.461 |
Algeria | Middle East and North Africa | 4.887 | 0.053 | 4.991 | 4.783 | 9.342 | 0.802 | 66.005 | 0.480 | -0.067 | 0.752 | 2.43 | 0.946 | 0.765 | 0.552 | 0.119 | 0.144 | 0.120 | 2.242 |
Albania | Central and Eastern Europe | 5.117 | 0.059 | 5.234 | 5.001 | 9.520 | 0.697 | 68.999 | 0.785 | -0.030 | 0.901 | 2.43 | 1.008 | 0.529 | 0.646 | 0.491 | 0.168 | 0.024 | 2.250 |
Afghanistan | South Asia | 2.523 | 0.038 | 2.596 | 2.449 | 7.695 | 0.463 | 52.493 | 0.382 | -0.102 | 0.924 | 2.43 | 0.370 | 0.000 | 0.126 | 0.000 | 0.122 | 0.010 | 1.895 |
149 rows × 19 columns
In [67]:
titanic.pclass.value_counts().sort_values()
Out[67]:
2 277
1 323
3 709
Name: pclass, dtype: int64
In [69]:
titanic.pclass.value_counts().sort_index().plot(kind = 'bar')
Out[69]:
<Axes: >

In [70]:
titanic.pclass.value_counts().sort_values().plot(kind = 'bar')
Out[70]:
<Axes: >

In [76]:
houses.bedrooms.value_counts().sort_index().plot(kind = 'bar')
Out[76]:
<Axes: >

In [77]:
countries.head()
Out[77]:
Country name | Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
0 | Finland | Western Europe | 7.842 | 0.032 | 7.904 | 7.780 | 10.775 | 0.954 | 72.0 | 0.949 | -0.098 | 0.186 | 2.43 | 1.446 | 1.106 | 0.741 | 0.691 | 0.124 | 0.481 | 3.253 |
1 | Denmark | Western Europe | 7.620 | 0.035 | 7.687 | 7.552 | 10.933 | 0.954 | 72.7 | 0.946 | 0.030 | 0.179 | 2.43 | 1.502 | 1.108 | 0.763 | 0.686 | 0.208 | 0.485 | 2.868 |
2 | Switzerland | Western Europe | 7.571 | 0.036 | 7.643 | 7.500 | 11.117 | 0.942 | 74.4 | 0.919 | 0.025 | 0.292 | 2.43 | 1.566 | 1.079 | 0.816 | 0.653 | 0.204 | 0.413 | 2.839 |
3 | Iceland | Western Europe | 7.554 | 0.059 | 7.670 | 7.438 | 10.878 | 0.983 | 73.0 | 0.955 | 0.160 | 0.673 | 2.43 | 1.482 | 1.172 | 0.772 | 0.698 | 0.293 | 0.170 | 2.967 |
4 | Netherlands | Western Europe | 7.464 | 0.027 | 7.518 | 7.410 | 10.932 | 0.942 | 72.4 | 0.913 | 0.175 | 0.338 | 2.43 | 1.501 | 1.079 | 0.753 | 0.647 | 0.302 | 0.384 | 2.798 |
In [78]:
countries['Ladder score']
Out[78]:
0 7.842
1 7.620
2 7.571
3 7.554
4 7.464
...
144 3.512
145 3.467
146 3.415
147 3.145
148 2.523
Name: Ladder score, Length: 149, dtype: float64
In [88]:
df = countries.set_index('Country name')df
Out[88]:
Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
Country name | |||||||||||||||||||
Finland | Western Europe | 7.842 | 0.032 | 7.904 | 7.780 | 10.775 | 0.954 | 72.000 | 0.949 | -0.098 | 0.186 | 2.43 | 1.446 | 1.106 | 0.741 | 0.691 | 0.124 | 0.481 | 3.253 |
Denmark | Western Europe | 7.620 | 0.035 | 7.687 | 7.552 | 10.933 | 0.954 | 72.700 | 0.946 | 0.030 | 0.179 | 2.43 | 1.502 | 1.108 | 0.763 | 0.686 | 0.208 | 0.485 | 2.868 |
Switzerland | Western Europe | 7.571 | 0.036 | 7.643 | 7.500 | 11.117 | 0.942 | 74.400 | 0.919 | 0.025 | 0.292 | 2.43 | 1.566 | 1.079 | 0.816 | 0.653 | 0.204 | 0.413 | 2.839 |
Iceland | Western Europe | 7.554 | 0.059 | 7.670 | 7.438 | 10.878 | 0.983 | 73.000 | 0.955 | 0.160 | 0.673 | 2.43 | 1.482 | 1.172 | 0.772 | 0.698 | 0.293 | 0.170 | 2.967 |
Netherlands | Western Europe | 7.464 | 0.027 | 7.518 | 7.410 | 10.932 | 0.942 | 72.400 | 0.913 | 0.175 | 0.338 | 2.43 | 1.501 | 1.079 | 0.753 | 0.647 | 0.302 | 0.384 | 2.798 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
Lesotho | Sub-Saharan Africa | 3.512 | 0.120 | 3.748 | 3.276 | 7.926 | 0.787 | 48.700 | 0.715 | -0.131 | 0.915 | 2.43 | 0.451 | 0.731 | 0.007 | 0.405 | 0.103 | 0.015 | 1.800 |
Botswana | Sub-Saharan Africa | 3.467 | 0.074 | 3.611 | 3.322 | 9.782 | 0.784 | 59.269 | 0.824 | -0.246 | 0.801 | 2.43 | 1.099 | 0.724 | 0.340 | 0.539 | 0.027 | 0.088 | 0.648 |
Rwanda | Sub-Saharan Africa | 3.415 | 0.068 | 3.548 | 3.282 | 7.676 | 0.552 | 61.400 | 0.897 | 0.061 | 0.167 | 2.43 | 0.364 | 0.202 | 0.407 | 0.627 | 0.227 | 0.493 | 1.095 |
Zimbabwe | Sub-Saharan Africa | 3.145 | 0.058 | 3.259 | 3.030 | 7.943 | 0.750 | 56.201 | 0.677 | -0.047 | 0.821 | 2.43 | 0.457 | 0.649 | 0.243 | 0.359 | 0.157 | 0.075 | 1.205 |
Afghanistan | South Asia | 2.523 | 0.038 | 2.596 | 2.449 | 7.695 | 0.463 | 52.493 | 0.382 | -0.102 | 0.924 | 2.43 | 0.370 | 0.000 | 0.126 | 0.000 | 0.122 | 0.010 | 1.895 |
149 rows × 19 columns
In [96]:
df.loc[['India','Canada','United Kingdom','United States']]
Out[96]:
Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
Country name | |||||||||||||||||||
India | South Asia | 3.819 | 0.026 | 3.869 | 3.769 | 8.755 | 0.603 | 60.633 | 0.893 | 0.089 | 0.774 | 2.43 | 0.741 | 0.316 | 0.383 | 0.622 | 0.246 | 0.106 | 1.405 |
Canada | North America and ANZ | 7.103 | 0.042 | 7.185 | 7.021 | 10.776 | 0.926 | 73.800 | 0.915 | 0.089 | 0.415 | 2.43 | 1.447 | 1.044 | 0.798 | 0.648 | 0.246 | 0.335 | 2.585 |
United Kingdom | Western Europe | 7.064 | 0.038 | 7.138 | 6.990 | 10.707 | 0.934 | 72.500 | 0.859 | 0.233 | 0.459 | 2.43 | 1.423 | 1.062 | 0.757 | 0.580 | 0.340 | 0.306 | 2.596 |
United States | North America and ANZ | 6.951 | 0.049 | 7.047 | 6.856 | 11.023 | 0.920 | 68.200 | 0.837 | 0.098 | 0.698 | 2.43 | 1.533 | 1.030 | 0.621 | 0.554 | 0.252 | 0.154 | 2.807 |
In [97]:
titanic.loc[[8,9,896]]
Out[97]:
pclass | survived | name | sex | age | sibsp | parch | ticket | fare | cabin | embarked | boat | body | home.dest | |
8 | 1 | 1 | Appleton, Mrs. Edward Dale (Charlotte Lamson) | female | 53 | 2 | 0 | 11769 | 51.4792 | C101 | S | D | ? | Bayside, Queens, NY |
9 | 1 | 0 | Artagaveytia, Mr. Ramon | male | 71 | 0 | 0 | PC 17609 | 49.5042 | ? | C | ? | 22 | Montevideo, Uruguay |
896 | 3 | 0 | Johnson, Mr. Alfred | male | 49 | 0 | 0 | LINE | 0 | ? | S | ? | ? | ? |
In [99]:
titanic.loc[5:10]
Out[99]:
pclass | survived | name | sex | age | sibsp | parch | ticket | fare | cabin | embarked | boat | body | home.dest | |
5 | 1 | 1 | Anderson, Mr. Harry | male | 48 | 0 | 0 | 19952 | 26.55 | E12 | S | 3 | ? | New York, NY |
6 | 1 | 1 | Andrews, Miss. Kornelia Theodosia | female | 63 | 1 | 0 | 13502 | 77.9583 | D7 | S | 10 | ? | Hudson, NY |
7 | 1 | 0 | Andrews, Mr. Thomas Jr | male | 39 | 0 | 0 | 112050 | 0 | A36 | S | ? | ? | Belfast, NI |
8 | 1 | 1 | Appleton, Mrs. Edward Dale (Charlotte Lamson) | female | 53 | 2 | 0 | 11769 | 51.4792 | C101 | S | D | ? | Bayside, Queens, NY |
9 | 1 | 0 | Artagaveytia, Mr. Ramon | male | 71 | 0 | 0 | PC 17609 | 49.5042 | ? | C | ? | 22 | Montevideo, Uruguay |
10 | 1 | 0 | Astor, Col. John Jacob | male | 47 | 1 | 0 | PC 17757 | 227.525 | C62 C64 | C | ? | 124 | New York, NY |
In [100]:
titanic.loc[1:100:2]
Out[100]:
pclass | survived | name | sex | age | sibsp | parch | ticket | fare | cabin | embarked | boat | body | home.dest | |
1 | 1 | 1 | Allison, Master. Hudson Trevor | male | 0.9167 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | 11 | ? | Montreal, PQ / Chesterville, ON |
3 | 1 | 0 | Allison, Mr. Hudson Joshua Creighton | male | 30 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | ? | 135 | Montreal, PQ / Chesterville, ON |
5 | 1 | 1 | Anderson, Mr. Harry | male | 48 | 0 | 0 | 19952 | 26.55 | E12 | S | 3 | ? | New York, NY |
7 | 1 | 0 | Andrews, Mr. Thomas Jr | male | 39 | 0 | 0 | 112050 | 0 | A36 | S | ? | ? | Belfast, NI |
9 | 1 | 0 | Artagaveytia, Mr. Ramon | male | 71 | 0 | 0 | PC 17609 | 49.5042 | ? | C | ? | 22 | Montevideo, Uruguay |
11 | 1 | 1 | Astor, Mrs. John Jacob (Madeleine Talmadge Force) | female | 18 | 1 | 0 | PC 17757 | 227.525 | C62 C64 | C | 4 | ? | New York, NY |
13 | 1 | 1 | Barber, Miss. Ellen 'Nellie' | female | 26 | 0 | 0 | 19877 | 78.85 | ? | S | 6 | ? | ? |
15 | 1 | 0 | Baumann, Mr. John D | male | ? | 0 | 0 | PC 17318 | 25.925 | ? | S | ? | ? | New York, NY |
17 | 1 | 1 | Baxter, Mrs. James (Helene DeLaudeniere Chaput) | female | 50 | 0 | 1 | PC 17558 | 247.5208 | B58 B60 | C | 6 | ? | Montreal, PQ |
19 | 1 | 0 | Beattie, Mr. Thomson | male | 36 | 0 | 0 | 13050 | 75.2417 | C6 | C | A | ? | Winnipeg, MN |
21 | 1 | 1 | Beckwith, Mrs. Richard Leonard (Sallie Monypeny) | female | 47 | 1 | 1 | 11751 | 52.5542 | D35 | S | 5 | ? | New York, NY |
23 | 1 | 1 | Bidois, Miss. Rosalie | female | 42 | 0 | 0 | PC 17757 | 227.525 | ? | C | 4 | ? | ? |
25 | 1 | 0 | Birnbaum, Mr. Jakob | male | 25 | 0 | 0 | 13905 | 26 | ? | C | ? | 148 | San Francisco, CA |
27 | 1 | 1 | Bishop, Mrs. Dickinson H (Helen Walton) | female | 19 | 1 | 0 | 11967 | 91.0792 | B49 | C | 7 | ? | Dowagiac, MI |
29 | 1 | 1 | Bjornstrom-Steffansson, Mr. Mauritz Hakan | male | 28 | 0 | 0 | 110564 | 26.55 | C52 | S | D | ? | Stockholm, Sweden / Washington, DC |
31 | 1 | 1 | Blank, Mr. Henry | male | 40 | 0 | 0 | 112277 | 31 | A31 | C | 7 | ? | Glen Ridge, NJ |
33 | 1 | 1 | Bonnell, Miss. Elizabeth | female | 58 | 0 | 0 | 113783 | 26.55 | C103 | S | 8 | ? | Birkdale, England Cleveland, Ohio |
35 | 1 | 1 | Bowen, Miss. Grace Scott | female | 45 | 0 | 0 | PC 17608 | 262.375 | ? | C | 4 | ? | Cooperstown, NY |
37 | 1 | 1 | Bradley, Mr. George ('George Arthur Brayton') | male | ? | 0 | 0 | 111427 | 26.55 | ? | S | 9 | ? | Los Angeles, CA |
39 | 1 | 0 | Brandeis, Mr. Emil | male | 48 | 0 | 0 | PC 17591 | 50.4958 | B10 | C | ? | 208 | Omaha, NE |
41 | 1 | 1 | Brown, Mrs. James Joseph (Margaret Tobin) | female | 44 | 0 | 0 | PC 17610 | 27.7208 | B4 | C | 6 | ? | Denver, CO |
43 | 1 | 1 | Bucknell, Mrs. William Robert (Emma Eliza Ward) | female | 60 | 0 | 0 | 11813 | 76.2917 | D15 | C | 8 | ? | Philadelphia, PA |
45 | 1 | 0 | Butt, Major. Archibald Willingham | male | 45 | 0 | 0 | 113050 | 26.55 | B38 | S | ? | ? | Washington, DC |
47 | 1 | 1 | Calderhead, Mr. Edward Pennington | male | 42 | 0 | 0 | PC 17476 | 26.2875 | E24 | S | 5 | ? | New York, NY |
49 | 1 | 1 | Cardeza, Mr. Thomas Drake Martinez | male | 36 | 0 | 1 | PC 17755 | 512.3292 | B51 B53 B55 | C | 3 | ? | Austria-Hungary / Germantown, Philadelphia, PA |
51 | 1 | 0 | Carlsson, Mr. Frans Olof | male | 33 | 0 | 0 | 695 | 5 | B51 B53 B55 | S | ? | ? | New York, NY |
53 | 1 | 0 | Carrau, Mr. Jose Pedro | male | 17 | 0 | 0 | 113059 | 47.1 | ? | S | ? | ? | Montevideo, Uruguay |
55 | 1 | 1 | Carter, Miss. Lucile Polk | female | 14 | 1 | 2 | 113760 | 120 | B96 B98 | S | 4 | ? | Bryn Mawr, PA |
57 | 1 | 1 | Carter, Mrs. William Ernest (Lucile Polk) | female | 36 | 1 | 2 | 113760 | 120 | B96 B98 | S | 4 | ? | Bryn Mawr, PA |
59 | 1 | 1 | Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genev... | female | ? | 0 | 0 | 17770 | 27.7208 | ? | C | 5 | ? | New York, NY |
61 | 1 | 1 | Cavendish, Mrs. Tyrell William (Julia Florence... | female | 76 | 1 | 0 | 19877 | 78.85 | C46 | S | 6 | ? | Little Onn Hall, Staffs |
63 | 1 | 1 | Chaffee, Mrs. Herbert Fuller (Carrie Constance... | female | 47 | 1 | 0 | W.E.P. 5734 | 61.175 | E31 | S | 4 | ? | Amenia, ND |
65 | 1 | 1 | Chambers, Mrs. Norman Campbell (Bertha Griggs) | female | 33 | 1 | 0 | 113806 | 53.1 | E8 | S | 5 | ? | New York, NY / Ithaca, NY |
67 | 1 | 1 | Cherry, Miss. Gladys | female | 30 | 0 | 0 | 110152 | 86.5 | B77 | S | 8 | ? | London, England |
69 | 1 | 1 | Chibnall, Mrs. (Edith Martha Bowerman) | female | ? | 0 | 1 | 113505 | 55 | E33 | S | 6 | ? | St Leonards-on-Sea, England Ohio |
71 | 1 | 0 | Clark, Mr. Walter Miller | male | 27 | 1 | 0 | 13508 | 136.7792 | C89 | C | ? | ? | Los Angeles, CA |
73 | 1 | 1 | Cleaver, Miss. Alice | female | 22 | 0 | 0 | 113781 | 151.55 | ? | S | 11 | ? | ? |
75 | 1 | 0 | Colley, Mr. Edward Pomeroy | male | 47 | 0 | 0 | 5727 | 25.5875 | E58 | S | ? | ? | Victoria, BC |
77 | 1 | 0 | Compton, Mr. Alexander Taylor Jr | male | 37 | 1 | 1 | PC 17756 | 83.1583 | E52 | C | ? | ? | Lakewood, NJ |
79 | 1 | 1 | Cornell, Mrs. Robert Clifford (Malvina Helen L... | female | 55 | 2 | 0 | 11770 | 25.7 | C101 | S | 2 | ? | New York, NY |
81 | 1 | 0 | Crosby, Capt. Edward Gifford | male | 70 | 1 | 1 | WE/P 5735 | 71 | B22 | S | ? | 269 | Milwaukee, WI |
83 | 1 | 1 | Crosby, Mrs. Edward Gifford (Catherine Elizabe... | female | 64 | 1 | 1 | 112901 | 26.55 | B26 | S | 7 | ? | Milwaukee, WI |
85 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | 4 | ? | New York, NY |
87 | 1 | 1 | Daniel, Mr. Robert Williams | male | 27 | 0 | 0 | 113804 | 30.5 | ? | S | 3 | ? | Philadelphia, PA |
89 | 1 | 0 | Davidson, Mr. Thornton | male | 31 | 1 | 0 | F.C. 12750 | 52 | B71 | S | ? | ? | Montreal, PQ |
91 | 1 | 1 | Dick, Mr. Albert Adrian | male | 31 | 1 | 0 | 17474 | 57 | B20 | S | 3 | ? | Calgary, AB |
93 | 1 | 1 | Dodge, Dr. Washington | male | 53 | 1 | 1 | 33638 | 81.8583 | A34 | S | 13 | ? | San Francisco, CA |
95 | 1 | 1 | Dodge, Mrs. Washington (Ruth Vidaver) | female | 54 | 1 | 1 | 33638 | 81.8583 | A34 | S | 5 | ? | San Francisco, CA |
97 | 1 | 1 | Douglas, Mrs. Frederick Charles (Mary Helene B... | female | 27 | 1 | 1 | PC 17558 | 247.5208 | B58 B60 | C | 6 | ? | Montreal, PQ |
99 | 1 | 1 | Duff Gordon, Lady. (Lucille Christiana Sutherl... | female | 48 | 1 | 0 | 11755 | 39.6 | A16 | C | 1 | ? | London / Paris |
In [107]:
df.sort_index().loc['Denmark':'France':2]
Out[107]:
Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
Country name | |||||||||||||||||||
Denmark | Western Europe | 7.620 | 0.035 | 7.687 | 7.552 | 10.933 | 0.954 | 72.700 | 0.946 | 0.030 | 0.179 | 2.43 | 1.502 | 1.108 | 0.763 | 0.686 | 0.208 | 0.485 | 2.868 |
Ecuador | Latin America and Caribbean | 5.764 | 0.057 | 5.875 | 5.653 | 9.313 | 0.821 | 68.800 | 0.842 | -0.124 | 0.843 | 2.43 | 0.935 | 0.806 | 0.640 | 0.560 | 0.107 | 0.062 | 2.653 |
El Salvador | Latin America and Caribbean | 6.061 | 0.065 | 6.188 | 5.933 | 9.054 | 0.762 | 66.402 | 0.888 | -0.110 | 0.688 | 2.43 | 0.845 | 0.675 | 0.565 | 0.615 | 0.116 | 0.160 | 3.085 |
Ethiopia | Sub-Saharan Africa | 4.275 | 0.051 | 4.374 | 4.175 | 7.694 | 0.764 | 59.000 | 0.752 | 0.082 | 0.761 | 2.43 | 0.370 | 0.679 | 0.331 | 0.451 | 0.241 | 0.114 | 2.089 |
France | Western Europe | 6.690 | 0.037 | 6.762 | 6.618 | 10.704 | 0.942 | 74.000 | 0.822 | -0.147 | 0.571 | 2.43 | 1.421 | 1.081 | 0.804 | 0.536 | 0.092 | 0.235 | 2.521 |
In [112]:
df.iloc[20:100:2]
Out[112]:
Regional indicator | Ladder score | Standard error of ladder score | upperwhisker | lowerwhisker | Logged GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Ladder score in Dystopia | Explained by: Log GDP per capita | Explained by: Social support | Explained by: Healthy life expectancy | Explained by: Freedom to make life choices | Explained by: Generosity | Explained by: Perceptions of corruption | Dystopia + residual | |
Country name | |||||||||||||||||||
France | Western Europe | 6.690 | 0.037 | 6.762 | 6.618 | 10.704 | 0.942 | 74.000 | 0.822 | -0.147 | 0.571 | 2.43 | 1.421 | 1.081 | 0.804 | 0.536 | 0.092 | 0.235 | 2.521 |
Malta | Western Europe | 6.602 | 0.044 | 6.688 | 6.516 | 10.674 | 0.931 | 72.200 | 0.927 | 0.133 | 0.653 | 2.43 | 1.411 | 1.055 | 0.747 | 0.664 | 0.275 | 0.183 | 2.268 |
United Arab Emirates | Middle East and North Africa | 6.561 | 0.039 | 6.637 | 6.484 | 11.085 | 0.844 | 67.333 | 0.932 | 0.074 | 0.589 | 2.43 | 1.555 | 0.860 | 0.594 | 0.670 | 0.236 | 0.223 | 2.422 |
Spain | Western Europe | 6.491 | 0.042 | 6.574 | 6.408 | 10.571 | 0.932 | 74.700 | 0.761 | -0.081 | 0.745 | 2.43 | 1.375 | 1.057 | 0.826 | 0.462 | 0.135 | 0.124 | 2.513 |
Slovenia | Central and Eastern Europe | 6.461 | 0.043 | 6.546 | 6.376 | 10.529 | 0.948 | 71.400 | 0.949 | -0.101 | 0.806 | 2.43 | 1.360 | 1.093 | 0.722 | 0.690 | 0.122 | 0.085 | 2.388 |
Uruguay | Latin America and Caribbean | 6.431 | 0.046 | 6.521 | 6.341 | 9.966 | 0.925 | 69.100 | 0.896 | -0.092 | 0.590 | 2.43 | 1.164 | 1.042 | 0.649 | 0.625 | 0.128 | 0.223 | 2.600 |
Kosovo | Central and Eastern Europe | 6.372 | 0.059 | 6.487 | 6.257 | 9.318 | 0.821 | 63.813 | 0.869 | 0.257 | 0.917 | 2.43 | 0.937 | 0.807 | 0.483 | 0.593 | 0.356 | 0.014 | 3.182 |
Brazil | Latin America and Caribbean | 6.330 | 0.043 | 6.415 | 6.245 | 9.577 | 0.882 | 66.601 | 0.804 | -0.071 | 0.756 | 2.43 | 1.028 | 0.944 | 0.571 | 0.514 | 0.142 | 0.117 | 3.015 |
Jamaica | Latin America and Caribbean | 6.309 | 0.156 | 6.615 | 6.004 | 9.186 | 0.877 | 67.500 | 0.890 | -0.137 | 0.884 | 2.43 | 0.891 | 0.932 | 0.599 | 0.618 | 0.099 | 0.035 | 3.135 |
Cyprus | Western Europe | 6.223 | 0.049 | 6.319 | 6.128 | 10.576 | 0.802 | 73.898 | 0.763 | -0.015 | 0.844 | 2.43 | 1.377 | 0.765 | 0.801 | 0.464 | 0.178 | 0.061 | 2.578 |
Panama | Latin America and Caribbean | 6.180 | 0.073 | 6.323 | 6.036 | 10.350 | 0.896 | 69.652 | 0.872 | -0.166 | 0.856 | 2.43 | 1.298 | 0.976 | 0.667 | 0.596 | 0.079 | 0.053 | 2.509 |
Chile | Latin America and Caribbean | 6.172 | 0.046 | 6.262 | 6.081 | 10.071 | 0.882 | 70.000 | 0.742 | -0.044 | 0.830 | 2.43 | 1.200 | 0.946 | 0.678 | 0.438 | 0.159 | 0.070 | 2.682 |
Kazakhstan | Commonwealth of Independent States | 6.152 | 0.047 | 6.243 | 6.060 | 10.155 | 0.952 | 65.200 | 0.853 | -0.069 | 0.733 | 2.43 | 1.230 | 1.103 | 0.527 | 0.573 | 0.143 | 0.132 | 2.446 |
Kuwait | Middle East and North Africa | 6.106 | 0.066 | 6.235 | 5.977 | 10.817 | 0.843 | 66.900 | 0.867 | -0.104 | 0.736 | 2.43 | 1.461 | 0.857 | 0.580 | 0.591 | 0.120 | 0.130 | 2.368 |
El Salvador | Latin America and Caribbean | 6.061 | 0.065 | 6.188 | 5.933 | 9.054 | 0.762 | 66.402 | 0.888 | -0.110 | 0.688 | 2.43 | 0.845 | 0.675 | 0.565 | 0.615 | 0.116 | 0.160 | 3.085 |
Latvia | Central and Eastern Europe | 6.032 | 0.036 | 6.103 | 5.961 | 10.315 | 0.927 | 67.100 | 0.715 | -0.162 | 0.800 | 2.43 | 1.285 | 1.047 | 0.587 | 0.405 | 0.082 | 0.089 | 2.536 |
Hungary | Central and Eastern Europe | 5.992 | 0.047 | 6.085 | 5.899 | 10.358 | 0.943 | 68.000 | 0.755 | -0.186 | 0.876 | 2.43 | 1.301 | 1.083 | 0.615 | 0.454 | 0.067 | 0.040 | 2.432 |
Nicaragua | Latin America and Caribbean | 5.972 | 0.083 | 6.134 | 5.810 | 8.620 | 0.864 | 67.657 | 0.836 | 0.020 | 0.664 | 2.43 | 0.693 | 0.904 | 0.604 | 0.553 | 0.201 | 0.176 | 2.841 |
Argentina | Latin America and Caribbean | 5.929 | 0.056 | 6.040 | 5.819 | 9.962 | 0.898 | 69.000 | 0.828 | -0.182 | 0.834 | 2.43 | 1.162 | 0.980 | 0.646 | 0.544 | 0.069 | 0.067 | 2.461 |
Honduras | Latin America and Caribbean | 5.919 | 0.082 | 6.081 | 5.758 | 8.648 | 0.812 | 67.300 | 0.857 | 0.081 | 0.809 | 2.43 | 0.703 | 0.787 | 0.593 | 0.578 | 0.241 | 0.083 | 2.934 |
Philippines | Southeast Asia | 5.880 | 0.052 | 5.982 | 5.778 | 9.076 | 0.830 | 62.000 | 0.917 | -0.097 | 0.742 | 2.43 | 0.853 | 0.828 | 0.426 | 0.651 | 0.125 | 0.126 | 2.872 |
Peru | Latin America and Caribbean | 5.840 | 0.075 | 5.988 | 5.692 | 9.458 | 0.832 | 68.250 | 0.822 | -0.154 | 0.891 | 2.43 | 0.986 | 0.833 | 0.623 | 0.536 | 0.087 | 0.031 | 2.744 |
Moldova | Commonwealth of Independent States | 5.766 | 0.046 | 5.856 | 5.677 | 9.454 | 0.857 | 65.699 | 0.822 | -0.079 | 0.918 | 2.43 | 0.985 | 0.888 | 0.542 | 0.536 | 0.137 | 0.013 | 2.665 |
Kyrgyzstan | Commonwealth of Independent States | 5.744 | 0.046 | 5.834 | 5.653 | 8.538 | 0.893 | 64.401 | 0.935 | 0.119 | 0.908 | 2.43 | 0.665 | 0.971 | 0.501 | 0.673 | 0.266 | 0.020 | 2.648 |
Bolivia | Latin America and Caribbean | 5.716 | 0.053 | 5.819 | 5.613 | 9.046 | 0.810 | 63.901 | 0.875 | -0.077 | 0.839 | 2.43 | 0.842 | 0.782 | 0.486 | 0.600 | 0.138 | 0.064 | 2.805 |
Paraguay | Latin America and Caribbean | 5.653 | 0.092 | 5.832 | 5.473 | 9.448 | 0.893 | 65.900 | 0.876 | 0.028 | 0.882 | 2.43 | 0.983 | 0.970 | 0.549 | 0.602 | 0.206 | 0.037 | 2.306 |
Dominican Republic | Latin America and Caribbean | 5.545 | 0.071 | 5.685 | 5.405 | 9.802 | 0.853 | 66.102 | 0.860 | -0.133 | 0.714 | 2.43 | 1.106 | 0.879 | 0.555 | 0.581 | 0.101 | 0.144 | 2.178 |
Belarus | Commonwealth of Independent States | 5.534 | 0.047 | 5.625 | 5.442 | 9.853 | 0.910 | 66.253 | 0.650 | -0.180 | 0.627 | 2.43 | 1.124 | 1.007 | 0.560 | 0.326 | 0.070 | 0.199 | 2.247 |
Hong Kong S.A.R. of China | East Asia | 5.477 | 0.049 | 5.573 | 5.380 | 11.000 | 0.836 | 76.820 | 0.717 | 0.067 | 0.403 | 2.43 | 1.525 | 0.841 | 0.893 | 0.408 | 0.232 | 0.342 | 1.236 |
Vietnam | Southeast Asia | 5.411 | 0.039 | 5.488 | 5.334 | 8.973 | 0.850 | 68.034 | 0.940 | -0.098 | 0.796 | 2.43 | 0.817 | 0.873 | 0.616 | 0.679 | 0.124 | 0.091 | 2.211 |
Malaysia | Southeast Asia | 5.384 | 0.049 | 5.480 | 5.289 | 10.238 | 0.817 | 67.102 | 0.895 | 0.125 | 0.839 | 2.43 | 1.259 | 0.797 | 0.587 | 0.624 | 0.270 | 0.064 | 1.784 |
Congo (Brazzaville) | Sub-Saharan Africa | 5.342 | 0.097 | 5.533 | 5.151 | 8.117 | 0.636 | 58.221 | 0.695 | -0.068 | 0.745 | 2.43 | 0.518 | 0.392 | 0.307 | 0.381 | 0.144 | 0.124 | 3.476 |
Ivory Coast | Sub-Saharan Africa | 5.306 | 0.078 | 5.460 | 5.152 | 8.551 | 0.644 | 50.114 | 0.741 | -0.016 | 0.794 | 2.43 | 0.669 | 0.409 | 0.052 | 0.438 | 0.177 | 0.092 | 3.469 |
Nepal | South Asia | 5.269 | 0.070 | 5.406 | 5.132 | 8.120 | 0.774 | 64.233 | 0.782 | 0.152 | 0.727 | 2.43 | 0.519 | 0.702 | 0.496 | 0.488 | 0.287 | 0.135 | 2.642 |
Maldives | South Asia | 5.198 | 0.072 | 5.339 | 5.057 | 9.826 | 0.913 | 70.600 | 0.854 | 0.024 | 0.825 | 2.43 | 1.115 | 1.015 | 0.697 | 0.575 | 0.204 | 0.073 | 1.520 |
Cameroon | Sub-Saharan Africa | 5.142 | 0.074 | 5.288 | 4.996 | 8.189 | 0.710 | 53.515 | 0.731 | 0.026 | 0.848 | 2.43 | 0.543 | 0.556 | 0.159 | 0.425 | 0.205 | 0.058 | 3.195 |
Albania | Central and Eastern Europe | 5.117 | 0.059 | 5.234 | 5.001 | 9.520 | 0.697 | 68.999 | 0.785 | -0.030 | 0.901 | 2.43 | 1.008 | 0.529 | 0.646 | 0.491 | 0.168 | 0.024 | 2.250 |
Ghana | Sub-Saharan Africa | 5.088 | 0.067 | 5.219 | 4.958 | 8.580 | 0.727 | 57.586 | 0.807 | 0.123 | 0.848 | 2.43 | 0.680 | 0.595 | 0.287 | 0.517 | 0.268 | 0.058 | 2.684 |
Turkmenistan | Commonwealth of Independent States | 5.066 | 0.036 | 5.136 | 4.996 | 9.629 | 0.983 | 62.409 | 0.877 | 0.273 | 0.888 | 2.43 | 1.046 | 1.172 | 0.439 | 0.602 | 0.366 | 0.033 | 1.409 |
Benin | Sub-Saharan Africa | 5.045 | 0.073 | 5.189 | 4.901 | 8.087 | 0.489 | 54.713 | 0.757 | -0.034 | 0.661 | 2.43 | 0.507 | 0.058 | 0.196 | 0.457 | 0.166 | 0.178 | 3.482 |
In [120]:
house = houses.sort_index(ascending = False)house
Out[120]:
id | date | price | bedrooms | bathrooms | sqft_living | sqft_lot | floors | waterfront | view | ... | grade | sqft_above | sqft_basement | yr_built | yr_renovated | zipcode | lat | long | sqft_living15 | sqft_lot15 | |
21612 | 1523300157 | 20141015T000000 | 325000.0 | 2 | 0.75 | 1020 | 1076 | 2.0 | 0 | 0 | ... | 7 | 1020 | 0 | 2008 | 0 | 98144 | 47.5941 | -122.299 | 1020 | 1357 |
21611 | 291310100 | 20150116T000000 | 400000.0 | 3 | 2.50 | 1600 | 2388 | 2.0 | 0 | 0 | ... | 8 | 1600 | 0 | 2004 | 0 | 98027 | 47.5345 | -122.069 | 1410 | 1287 |
21610 | 1523300141 | 20140623T000000 | 402101.0 | 2 | 0.75 | 1020 | 1350 | 2.0 | 0 | 0 | ... | 7 | 1020 | 0 | 2009 | 0 | 98144 | 47.5944 | -122.299 | 1020 | 2007 |
21609 | 6600060120 | 20150223T000000 | 400000.0 | 4 | 2.50 | 2310 | 5813 | 2.0 | 0 | 0 | ... | 8 | 2310 | 0 | 2014 | 0 | 98146 | 47.5107 | -122.362 | 1830 | 7200 |
21608 | 263000018 | 20140521T000000 | 360000.0 | 3 | 2.50 | 1530 | 1131 | 3.0 | 0 | 0 | ... | 8 | 1530 | 0 | 2009 | 0 | 98103 | 47.6993 | -122.346 | 1530 | 1509 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
4 | 1954400510 | 20150218T000000 | 510000.0 | 3 | 2.00 | 1680 | 8080 | 1.0 | 0 | 0 | ... | 8 | 1680 | 0 | 1987 | 0 | 98074 | 47.6168 | -122.045 | 1800 | 7503 |
3 | 2487200875 | 20141209T000000 | 604000.0 | 4 | 3.00 | 1960 | 5000 | 1.0 | 0 | 0 | ... | 7 | 1050 | 910 | 1965 | 0 | 98136 | 47.5208 | -122.393 | 1360 | 5000 |
2 | 5631500400 | 20150225T000000 | 180000.0 | 2 | 1.00 | 770 | 10000 | 1.0 | 0 | 0 | ... | 6 | 770 | 0 | 1933 | 0 | 98028 | 47.7379 | -122.233 | 2720 | 8062 |
1 | 6414100192 | 20141209T000000 | 538000.0 | 3 | 2.25 | 2570 | 7242 | 2.0 | 0 | 0 | ... | 7 | 2170 | 400 | 1951 | 1991 | 98125 | 47.7210 | -122.319 | 1690 | 7639 |
0 | 7129300520 | 20141013T000000 | 221900.0 | 3 | 1.00 | 1180 | 5650 | 1.0 | 0 | 0 | ... | 7 | 1180 | 0 | 1955 | 0 | 98178 | 47.5112 | -122.257 | 1340 | 5650 |
21613 rows × 21 columns
In [126]:
house.loc[21612:21611, ['price', 'bedrooms']]
Out[126]:
price | bedrooms | |
21612 | 325000.0 | 2 |
21611 | 400000.0 | 3 |
In [129]:
titanic.loc[50:60:2, ['name', 'sex', 'age']]
Out[129]:
name | sex | age | |
50 | Cardeza, Mrs. James Warburton Martinez (Charlo... | female | 58 |
52 | Carrau, Mr. Francisco M | male | 28 |
54 | Carter, Master. William Thornton II | male | 11 |
56 | Carter, Mr. William Ernest | male | 36 |
58 | Case, Mr. Howard Brown | male | 49 |
60 | Cavendish, Mr. Tyrell William | male | 36 |
In [132]:
df.loc['Denmark':'Canada', 'Ladder score']
Out[132]:
Country name
Denmark 7.620
Switzerland 7.571
Iceland 7.554
Netherlands 7.464
Norway 7.392
Sweden 7.363
Luxembourg 7.324
New Zealand 7.277
Austria 7.268
Australia 7.183
Israel 7.157
Germany 7.155
Canada 7.103
Name: Ladder score, dtype: float64
In [139]:
houses = pd.read_csv('kc_house_data.csv')houses
Out[139]:
id | date | price | bedrooms | bathrooms | sqft_living | sqft_lot | floors | waterfront | view | ... | grade | sqft_above | sqft_basement | yr_built | yr_renovated | zipcode | lat | long | sqft_living15 | sqft_lot15 | |
0 | 7129300520 | 20141013T000000 | 221900.0 | 3 | 1.00 | 1180 | 5650 | 1.0 | 0 | 0 | ... | 7 | 1180 | 0 | 1955 | 0 | 98178 | 47.5112 | -122.257 | 1340 | 5650 |
1 | 6414100192 | 20141209T000000 | 538000.0 | 3 | 2.25 | 2570 | 7242 | 2.0 | 0 | 0 | ... | 7 | 2170 | 400 | 1951 | 1991 | 98125 | 47.7210 | -122.319 | 1690 | 7639 |
2 | 5631500400 | 20150225T000000 | 180000.0 | 2 | 1.00 | 770 | 10000 | 1.0 | 0 | 0 | ... | 6 | 770 | 0 | 1933 | 0 | 98028 | 47.7379 | -122.233 | 2720 | 8062 |
3 | 2487200875 | 20141209T000000 | 604000.0 | 4 | 3.00 | 1960 | 5000 | 1.0 | 0 | 0 | ... | 7 | 1050 | 910 | 1965 | 0 | 98136 | 47.5208 | -122.393 | 1360 | 5000 |
4 | 1954400510 | 20150218T000000 | 510000.0 | 3 | 2.00 | 1680 | 8080 | 1.0 | 0 | 0 | ... | 8 | 1680 | 0 | 1987 | 0 | 98074 | 47.6168 | -122.045 | 1800 | 7503 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
21608 | 263000018 | 20140521T000000 | 360000.0 | 3 | 2.50 | 1530 | 1131 | 3.0 | 0 | 0 | ... | 8 | 1530 | 0 | 2009 | 0 | 98103 | 47.6993 | -122.346 | 1530 | 1509 |
21609 | 6600060120 | 20150223T000000 | 400000.0 | 4 | 2.50 | 2310 | 5813 | 2.0 | 0 | 0 | ... | 8 | 2310 | 0 | 2014 | 0 | 98146 | 47.5107 | -122.362 | 1830 | 7200 |
21610 | 1523300141 | 20140623T000000 | 402101.0 | 2 | 0.75 | 1020 | 1350 | 2.0 | 0 | 0 | ... | 7 | 1020 | 0 | 2009 | 0 | 98144 | 47.5944 | -122.299 | 1020 | 2007 |
21611 | 291310100 | 20150116T000000 | 400000.0 | 3 | 2.50 | 1600 | 2388 | 2.0 | 0 | 0 | ... | 8 | 1600 | 0 | 2004 | 0 | 98027 | 47.5345 | -122.069 | 1410 | 1287 |
21612 | 1523300157 | 20141015T000000 | 325000.0 | 2 | 0.75 | 1020 | 1076 | 2.0 | 0 | 0 | ... | 7 | 1020 | 0 | 2008 | 0 | 98144 | 47.5941 | -122.299 | 1020 | 1357 |
21613 rows × 21 columns
In [143]:
houses['bedrooms'].value_counts().loc[33]
Out[143]:
1
In [145]:
titanic.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1309 entries, 0 to 1308
Data columns (total 14 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 pclass 1309 non-null int64
1 survived 1309 non-null int64
2 name 1309 non-null object
3 sex 1309 non-null object
4 age 1309 non-null object
5 sibsp 1309 non-null int64
6 parch 1309 non-null int64
7 ticket 1309 non-null object
8 fare 1309 non-null object
9 cabin 1309 non-null object
10 embarked 1309 non-null object
11 boat 1309 non-null object
12 body 1309 non-null object
13 home.dest 1309 non-null object
dtypes: int64(4), object(10)
memory usage: 143.3+ KB
In [151]:
titanic['age'].value_counts().iloc[0:5]
Out[151]:
? 263
24 47
22 43
21 41
30 40
Name: age, dtype: int64
In [152]:
pokemon = pd.read_csv('pokemon.csv')pokemon
Out[152]:
Num | Name | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
0 | 1 | Bulbasaur | Grass | Poison | 318 | 45 | 49 | 49 | 65 | 65 | 45 | 1 | False |
1 | 2 | Ivysaur | Grass | Poison | 405 | 60 | 62 | 63 | 80 | 80 | 60 | 2 | False |
2 | 3 | Venusaur | Grass | Poison | 525 | 80 | 82 | 83 | 100 | 100 | 80 | 3 | False |
3 | 4 | Charmander | Fire | NaN | 309 | 39 | 52 | 43 | 60 | 50 | 65 | 1 | False |
4 | 5 | Charmeleon | Fire | NaN | 405 | 58 | 64 | 58 | 80 | 65 | 80 | 2 | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
146 | 147 | Dratini | Dragon | NaN | 300 | 41 | 64 | 45 | 50 | 50 | 50 | 1 | False |
147 | 148 | Dragonair | Dragon | NaN | 420 | 61 | 84 | 65 | 70 | 70 | 70 | 2 | False |
148 | 149 | Dragonite | Dragon | Flying | 600 | 91 | 134 | 95 | 100 | 100 | 80 | 3 | False |
149 | 150 | Mewtwo | Psychic | NaN | 680 | 106 | 110 | 90 | 154 | 90 | 130 | 1 | True |
150 | 151 | Mew | Psychic | NaN | 600 | 100 | 100 | 100 | 100 | 100 | 100 | 1 | False |
151 rows × 13 columns
In [153]:
pokemon.set_index('Name')
Out[153]:
Num | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
Name | ||||||||||||
Bulbasaur | 1 | Grass | Poison | 318 | 45 | 49 | 49 | 65 | 65 | 45 | 1 | False |
Ivysaur | 2 | Grass | Poison | 405 | 60 | 62 | 63 | 80 | 80 | 60 | 2 | False |
Venusaur | 3 | Grass | Poison | 525 | 80 | 82 | 83 | 100 | 100 | 80 | 3 | False |
Charmander | 4 | Fire | NaN | 309 | 39 | 52 | 43 | 60 | 50 | 65 | 1 | False |
Charmeleon | 5 | Fire | NaN | 405 | 58 | 64 | 58 | 80 | 65 | 80 | 2 | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
Dratini | 147 | Dragon | NaN | 300 | 41 | 64 | 45 | 50 | 50 | 50 | 1 | False |
Dragonair | 148 | Dragon | NaN | 420 | 61 | 84 | 65 | 70 | 70 | 70 | 2 | False |
Dragonite | 149 | Dragon | Flying | 600 | 91 | 134 | 95 | 100 | 100 | 80 | 3 | False |
Mewtwo | 150 | Psychic | NaN | 680 | 106 | 110 | 90 | 154 | 90 | 130 | 1 | True |
Mew | 151 | Psychic | NaN | 600 | 100 | 100 | 100 | 100 | 100 | 100 | 1 | False |
151 rows × 12 columns
In [158]:
pokemon.sort_values('Attack', ascending = False)
Out[158]:
Num | Name | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
148 | 149 | Dragonite | Dragon | Flying | 600 | 91 | 134 | 95 | 100 | 100 | 80 | 3 | False |
111 | 112 | Rhydon | Ground | Rock | 485 | 105 | 130 | 120 | 45 | 45 | 40 | 2 | False |
135 | 136 | Flareon | Fire | NaN | 525 | 65 | 130 | 60 | 95 | 110 | 65 | 2 | False |
67 | 68 | Machamp | Fighting | NaN | 505 | 90 | 130 | 80 | 65 | 85 | 55 | 3 | False |
98 | 99 | Kingler | Water | NaN | 475 | 55 | 130 | 115 | 50 | 50 | 75 | 2 | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
13 | 14 | Kakuna | Bug | Poison | 205 | 45 | 25 | 50 | 25 | 25 | 35 | 2 | False |
62 | 63 | Abra | Psychic | NaN | 310 | 25 | 20 | 15 | 105 | 55 | 90 | 1 | False |
10 | 11 | Metapod | Bug | NaN | 205 | 50 | 20 | 55 | 25 | 25 | 30 | 2 | False |
128 | 129 | Magikarp | Water | NaN | 200 | 20 | 10 | 55 | 15 | 20 | 80 | 1 | False |
112 | 113 | Chansey | Normal | NaN | 450 | 250 | 5 | 5 | 35 | 105 | 50 | 1 | False |
151 rows × 13 columns
In [164]:
pokemon.sort_values(['Total','Attack','Sp. Atk'], ascending = False)
Out[164]:
Num | Name | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
149 | 150 | Mewtwo | Psychic | NaN | 680 | 106 | 110 | 90 | 154 | 90 | 130 | 1 | True |
148 | 149 | Dragonite | Dragon | Flying | 600 | 91 | 134 | 95 | 100 | 100 | 80 | 3 | False |
150 | 151 | Mew | Psychic | NaN | 600 | 100 | 100 | 100 | 100 | 100 | 100 | 1 | False |
145 | 146 | Moltres | Fire | Flying | 580 | 90 | 100 | 90 | 125 | 85 | 90 | 1 | True |
144 | 145 | Zapdos | Electric | Flying | 580 | 90 | 90 | 85 | 125 | 90 | 100 | 1 | True |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
13 | 14 | Kakuna | Bug | Poison | 205 | 45 | 25 | 50 | 25 | 25 | 35 | 2 | False |
10 | 11 | Metapod | Bug | NaN | 205 | 50 | 20 | 55 | 25 | 25 | 30 | 2 | False |
128 | 129 | Magikarp | Water | NaN | 200 | 20 | 10 | 55 | 15 | 20 | 80 | 1 | False |
12 | 13 | Weedle | Bug | Poison | 195 | 40 | 35 | 30 | 20 | 20 | 50 | 1 | False |
9 | 10 | Caterpie | Bug | NaN | 195 | 45 | 30 | 35 | 20 | 20 | 45 | 1 | False |
151 rows × 13 columns
In [169]:
pokemon.set_index('Name').sort_index().tail(20)
Out[169]:
Num | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
Name | ||||||||||||
Staryu | 120 | Water | NaN | 340 | 30 | 45 | 55 | 70 | 55 | 85 | 1 | False |
Tangela | 114 | Grass | NaN | 435 | 65 | 55 | 115 | 100 | 40 | 60 | 1 | False |
Tauros | 128 | Normal | NaN | 490 | 75 | 100 | 95 | 40 | 70 | 110 | 1 | False |
Tentacool | 72 | Water | Poison | 335 | 40 | 40 | 35 | 50 | 100 | 70 | 1 | False |
Tentacruel | 73 | Water | Poison | 515 | 80 | 70 | 65 | 80 | 120 | 100 | 2 | False |
Vaporeon | 134 | Water | NaN | 525 | 130 | 65 | 60 | 110 | 95 | 65 | 2 | False |
Venomoth | 49 | Bug | Poison | 450 | 70 | 65 | 60 | 90 | 75 | 90 | 2 | False |
Venonat | 48 | Bug | Poison | 305 | 60 | 55 | 50 | 40 | 55 | 45 | 1 | False |
Venusaur | 3 | Grass | Poison | 525 | 80 | 82 | 83 | 100 | 100 | 80 | 3 | False |
Victreebel | 71 | Grass | Poison | 490 | 80 | 105 | 65 | 100 | 70 | 70 | 3 | False |
Vileplume | 45 | Grass | Poison | 490 | 75 | 80 | 85 | 110 | 90 | 50 | 3 | False |
Voltorb | 100 | Electric | NaN | 330 | 40 | 30 | 50 | 55 | 55 | 100 | 1 | False |
Vulpix | 37 | Fire | NaN | 299 | 38 | 41 | 40 | 50 | 65 | 65 | 1 | False |
Wartortle | 8 | Water | NaN | 405 | 59 | 63 | 80 | 65 | 80 | 58 | 2 | False |
Weedle | 13 | Bug | Poison | 195 | 40 | 35 | 30 | 20 | 20 | 50 | 1 | False |
Weepinbell | 70 | Grass | Poison | 390 | 65 | 90 | 50 | 85 | 45 | 55 | 2 | False |
Weezing | 110 | Poison | NaN | 490 | 65 | 90 | 120 | 85 | 70 | 60 | 2 | False |
Wigglytuff | 40 | Normal | Fairy | 435 | 140 | 70 | 45 | 85 | 50 | 45 | 2 | False |
Zapdos | 145 | Electric | Flying | 580 | 90 | 90 | 85 | 125 | 90 | 100 | 1 | True |
Zubat | 41 | Poison | Flying | 245 | 40 | 45 | 35 | 30 | 40 | 55 | 1 | False |
In [172]:
pok_df = pokemon.sort_values('Speed',ascending = False)pok_df
Out[172]:
Num | Name | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
100 | 101 | Electrode | Electric | NaN | 480 | 60 | 50 | 70 | 80 | 80 | 140 | 2 | False |
141 | 142 | Aerodactyl | Rock | Flying | 515 | 80 | 105 | 65 | 60 | 75 | 130 | 1 | False |
134 | 135 | Jolteon | Electric | NaN | 525 | 65 | 65 | 60 | 110 | 95 | 130 | 2 | False |
149 | 150 | Mewtwo | Psychic | NaN | 680 | 106 | 110 | 90 | 154 | 90 | 130 | 1 | True |
64 | 65 | Alakazam | Psychic | NaN | 500 | 55 | 50 | 45 | 135 | 95 | 120 | 3 | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
45 | 46 | Paras | Bug | Grass | 285 | 35 | 70 | 55 | 45 | 55 | 25 | 1 | False |
110 | 111 | Rhyhorn | Ground | Rock | 345 | 80 | 85 | 95 | 30 | 30 | 25 | 1 | False |
38 | 39 | Jigglypuff | Normal | Fairy | 270 | 115 | 45 | 20 | 45 | 25 | 20 | 1 | False |
73 | 74 | Geodude | Rock | Ground | 300 | 40 | 80 | 100 | 30 | 30 | 20 | 1 | False |
78 | 79 | Slowpoke | Water | Psychic | 315 | 90 | 65 | 65 | 40 | 40 | 15 | 1 | False |
151 rows × 13 columns
In [181]:
pok_df.head(10).describe()
Out[181]:
Num | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | |
count | 10.000000 | 10.000000 | 10.000000 | 10.00000 | 10.000000 | 10.000000 | 10.000000 | 10.000000 | 10.000000 |
mean | 97.200000 | 504.000000 | 66.100000 | 79.50000 | 67.500000 | 88.400000 | 80.500000 | 122.000000 | 1.800000 |
std | 44.656218 | 72.141836 | 18.465884 | 21.53163 | 17.199806 | 36.999099 | 10.658851 | 10.055402 | 0.632456 |
min | 26.000000 | 405.000000 | 35.000000 | 50.00000 | 45.000000 | 40.000000 | 65.000000 | 110.000000 | 1.000000 |
25% | 56.000000 | 481.250000 | 60.000000 | 66.25000 | 56.250000 | 61.250000 | 71.250000 | 115.000000 | 1.250000 |
50% | 111.000000 | 495.000000 | 62.500000 | 77.50000 | 62.500000 | 85.000000 | 80.000000 | 120.000000 | 2.000000 |
75% | 133.250000 | 518.750000 | 72.500000 | 97.50000 | 81.250000 | 107.500000 | 88.750000 | 130.000000 | 2.000000 |
max | 150.000000 | 680.000000 | 106.000000 | 110.00000 | 95.000000 | 154.000000 | 95.000000 | 140.000000 | 3.000000 |
In [182]:
pok_df.head(20).describe()
Out[182]:
Num | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | |
count | 20.000000 | 20.00000 | 20.000000 | 20.000000 | 20.000000 | 20.000000 | 20.000000 | 20.000000 | 20.00000 |
mean | 87.900000 | 498.55000 | 68.100000 | 80.200000 | 67.600000 | 87.800000 | 82.300000 | 112.550000 | 1.85000 |
std | 44.750654 | 61.17832 | 16.861354 | 20.962623 | 17.135374 | 30.453761 | 14.382372 | 12.141599 | 0.67082 |
min | 18.000000 | 400.00000 | 35.000000 | 35.000000 | 30.000000 | 40.000000 | 61.000000 | 100.000000 | 1.00000 |
25% | 52.500000 | 479.75000 | 60.000000 | 68.750000 | 59.250000 | 64.000000 | 70.000000 | 104.000000 | 1.00000 |
50% | 86.000000 | 500.00000 | 65.000000 | 80.000000 | 65.000000 | 80.500000 | 80.000000 | 110.000000 | 2.00000 |
75% | 125.750000 | 515.00000 | 76.250000 | 100.000000 | 76.250000 | 102.500000 | 91.250000 | 120.000000 | 2.00000 |
max | 151.000000 | 680.00000 | 106.000000 | 110.000000 | 100.000000 | 154.000000 | 120.000000 | 140.000000 | 3.00000 |
In [183]:
pok_df.value_counts('Type 1')
Out[183]:
Type 1
Water 28
Normal 22
Poison 14
Bug 12
Fire 12
Grass 12
Electric 9
Rock 9
Ground 8
Psychic 8
Fighting 7
Dragon 3
Ghost 3
Fairy 2
Ice 2
dtype: int64
In [187]:
pokemon = pd.read_csv('pokemon.csv')pokemon
Out[187]:
Num | Name | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
0 | 1 | Bulbasaur | Grass | Poison | 318 | 45 | 49 | 49 | 65 | 65 | 45 | 1 | False |
1 | 2 | Ivysaur | Grass | Poison | 405 | 60 | 62 | 63 | 80 | 80 | 60 | 2 | False |
2 | 3 | Venusaur | Grass | Poison | 525 | 80 | 82 | 83 | 100 | 100 | 80 | 3 | False |
3 | 4 | Charmander | Fire | NaN | 309 | 39 | 52 | 43 | 60 | 50 | 65 | 1 | False |
4 | 5 | Charmeleon | Fire | NaN | 405 | 58 | 64 | 58 | 80 | 65 | 80 | 2 | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
146 | 147 | Dratini | Dragon | NaN | 300 | 41 | 64 | 45 | 50 | 50 | 50 | 1 | False |
147 | 148 | Dragonair | Dragon | NaN | 420 | 61 | 84 | 65 | 70 | 70 | 70 | 2 | False |
148 | 149 | Dragonite | Dragon | Flying | 600 | 91 | 134 | 95 | 100 | 100 | 80 | 3 | False |
149 | 150 | Mewtwo | Psychic | NaN | 680 | 106 | 110 | 90 | 154 | 90 | 130 | 1 | True |
150 | 151 | Mew | Psychic | NaN | 600 | 100 | 100 | 100 | 100 | 100 | 100 | 1 | False |
151 rows × 13 columns
In [189]:
pd = pokemon.set_index('Name')pd
Out[189]:
Num | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
Name | ||||||||||||
Bulbasaur | 1 | Grass | Poison | 318 | 45 | 49 | 49 | 65 | 65 | 45 | 1 | False |
Ivysaur | 2 | Grass | Poison | 405 | 60 | 62 | 63 | 80 | 80 | 60 | 2 | False |
Venusaur | 3 | Grass | Poison | 525 | 80 | 82 | 83 | 100 | 100 | 80 | 3 | False |
Charmander | 4 | Fire | NaN | 309 | 39 | 52 | 43 | 60 | 50 | 65 | 1 | False |
Charmeleon | 5 | Fire | NaN | 405 | 58 | 64 | 58 | 80 | 65 | 80 | 2 | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
Dratini | 147 | Dragon | NaN | 300 | 41 | 64 | 45 | 50 | 50 | 50 | 1 | False |
Dragonair | 148 | Dragon | NaN | 420 | 61 | 84 | 65 | 70 | 70 | 70 | 2 | False |
Dragonite | 149 | Dragon | Flying | 600 | 91 | 134 | 95 | 100 | 100 | 80 | 3 | False |
Mewtwo | 150 | Psychic | NaN | 680 | 106 | 110 | 90 | 154 | 90 | 130 | 1 | True |
Mew | 151 | Psychic | NaN | 600 | 100 | 100 | 100 | 100 | 100 | 100 | 1 | False |
151 rows × 12 columns
In [190]:
pd.loc['Diglett']
Out[190]:
Num 50
Type 1 Ground
Type 2 NaN
Total 265
HP 10
Attack 55
Defense 25
Sp. Atk 35
Sp. Def 45
Speed 95
Stage 1
Legendary False
Name: Diglett, dtype: object
In [202]:
pd.loc[['Eevee','Vulpix','Dragonair']]
Out[202]:
Num | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
Name | ||||||||||||
Eevee | 133 | Normal | NaN | 325 | 55 | 55 | 50 | 45 | 65 | 55 | 1 | False |
Vulpix | 37 | Fire | NaN | 299 | 38 | 41 | 40 | 50 | 65 | 65 | 1 | False |
Dragonair | 148 | Dragon | NaN | 420 | 61 | 84 | 65 | 70 | 70 | 70 | 2 | False |
In [205]:
pd.sort_index().loc['Charizard':'Charmeleon']
Out[205]:
Num | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
Name | ||||||||||||
Charizard | 6 | Fire | Flying | 534 | 78 | 84 | 78 | 109 | 85 | 100 | 3 | False |
Charmander | 4 | Fire | NaN | 309 | 39 | 52 | 43 | 60 | 50 | 65 | 1 | False |
Charmeleon | 5 | Fire | NaN | 405 | 58 | 64 | 58 | 80 | 65 | 80 | 2 | False |
In [207]:
pd.sort_index().iloc[[30,40,50]]
Out[207]:
Num | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
Name | ||||||||||||
Eevee | 133 | Normal | NaN | 325 | 55 | 55 | 50 | 45 | 65 | 55 | 1 | False |
Gengar | 94 | Ghost | Poison | 500 | 60 | 65 | 60 | 130 | 75 | 110 | 3 | False |
Gyarados | 130 | Water | Flying | 540 | 95 | 125 | 79 | 60 | 100 | 81 | 2 | False |
In [208]:
fish_pokemon = ['Magikarp','Goldeen','Horsea','Seaking','Seadra','Gyarados']pd.sort_index().loc[fish_pokemon]
Out[208]:
Num | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
Name | ||||||||||||
Magikarp | 129 | Water | NaN | 200 | 20 | 10 | 55 | 15 | 20 | 80 | 1 | False |
Goldeen | 118 | Water | NaN | 320 | 45 | 67 | 60 | 35 | 50 | 63 | 1 | False |
Horsea | 116 | Water | NaN | 295 | 30 | 40 | 70 | 70 | 25 | 60 | 1 | False |
Seaking | 119 | Water | NaN | 450 | 80 | 92 | 65 | 65 | 80 | 68 | 2 | False |
Seadra | 117 | Water | NaN | 440 | 55 | 65 | 95 | 95 | 45 | 85 | 2 | False |
Gyarados | 130 | Water | Flying | 540 | 95 | 125 | 79 | 60 | 100 | 81 | 2 | False |
In [213]:
water = pd.sort_index().loc[fish_pokemon]df = water.sort_values('Attack')
In [31]:
df
Out[31]:
Num | Name | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
0 | 1 | Bulbasaur | Grass | Poison | 318 | 45 | 49 | 49 | 65 | 65 | 45 | 1 | False |
1 | 2 | Ivysaur | Grass | Poison | 405 | 60 | 62 | 63 | 80 | 80 | 60 | 2 | False |
2 | 3 | Venusaur | Grass | Poison | 525 | 80 | 82 | 83 | 100 | 100 | 80 | 3 | False |
3 | 4 | Charmander | Fire | NaN | 309 | 39 | 52 | 43 | 60 | 50 | 65 | 1 | False |
4 | 5 | Charmeleon | Fire | NaN | 405 | 58 | 64 | 58 | 80 | 65 | 80 | 2 | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
146 | 147 | Dratini | Dragon | NaN | 300 | 41 | 64 | 45 | 50 | 50 | 50 | 1 | False |
147 | 148 | Dragonair | Dragon | NaN | 420 | 61 | 84 | 65 | 70 | 70 | 70 | 2 | False |
148 | 149 | Dragonite | Dragon | Flying | 600 | 91 | 134 | 95 | 100 | 100 | 80 | 3 | False |
149 | 150 | Mewtwo | Psychic | NaN | 680 | 106 | 110 | 90 | 154 | 90 | 130 | 1 | True |
150 | 151 | Mew | Psychic | NaN | 600 | 100 | 100 | 100 | 100 | 100 | 100 | 1 | False |
151 rows × 13 columns
In [32]:
pok_attack = df[['Attack']]pok_attack
Out[32]:
Attack | |
0 | 49 |
1 | 62 |
2 | 82 |
3 | 52 |
4 | 64 |
... | ... |
146 | 64 |
147 | 84 |
148 | 134 |
149 | 110 |
150 | 100 |
151 rows × 1 columns
In [235]:
pok_attack.plot(kind = 'bar', color = 'purple')
Out[235]:
<Axes: xlabel='Name'>

In [17]:
import pandas as pd
In [21]:
df = pd.read_csv('pokemon.csv')df
Out[21]:
Num | Name | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
0 | 1 | Bulbasaur | Grass | Poison | 318 | 45 | 49 | 49 | 65 | 65 | 45 | 1 | False |
1 | 2 | Ivysaur | Grass | Poison | 405 | 60 | 62 | 63 | 80 | 80 | 60 | 2 | False |
2 | 3 | Venusaur | Grass | Poison | 525 | 80 | 82 | 83 | 100 | 100 | 80 | 3 | False |
3 | 4 | Charmander | Fire | NaN | 309 | 39 | 52 | 43 | 60 | 50 | 65 | 1 | False |
4 | 5 | Charmeleon | Fire | NaN | 405 | 58 | 64 | 58 | 80 | 65 | 80 | 2 | False |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
146 | 147 | Dratini | Dragon | NaN | 300 | 41 | 64 | 45 | 50 | 50 | 50 | 1 | False |
147 | 148 | Dragonair | Dragon | NaN | 420 | 61 | 84 | 65 | 70 | 70 | 70 | 2 | False |
148 | 149 | Dragonite | Dragon | Flying | 600 | 91 | 134 | 95 | 100 | 100 | 80 | 3 | False |
149 | 150 | Mewtwo | Psychic | NaN | 680 | 106 | 110 | 90 | 154 | 90 | 130 | 1 | True |
150 | 151 | Mew | Psychic | NaN | 600 | 100 | 100 | 100 | 100 | 100 | 100 | 1 | False |
151 rows × 13 columns
In [25]:
pok_att = df.set_index('Name').sort_values('Attack',ascending = False).head(10)pok_att
Out[25]:
Num | Type 1 | Type 2 | Total | HP | Attack | Defense | Sp. Atk | Sp. Def | Speed | Stage | Legendary | |
Name | ||||||||||||
Dragonite | 149 | Dragon | Flying | 600 | 91 | 134 | 95 | 100 | 100 | 80 | 3 | False |
Rhydon | 112 | Ground | Rock | 485 | 105 | 130 | 120 | 45 | 45 | 40 | 2 | False |
Flareon | 136 | Fire | NaN | 525 | 65 | 130 | 60 | 95 | 110 | 65 | 2 | False |
Machamp | 68 | Fighting | NaN | 505 | 90 | 130 | 80 | 65 | 85 | 55 | 3 | False |
Kingler | 99 | Water | NaN | 475 | 55 | 130 | 115 | 50 | 50 | 75 | 2 | False |
Pinsir | 127 | Bug | NaN | 500 | 65 | 125 | 100 | 55 | 70 | 85 | 1 | False |
Gyarados | 130 | Water | Flying | 540 | 95 | 125 | 79 | 60 | 100 | 81 | 2 | False |
Hitmonlee | 106 | Fighting | NaN | 455 | 50 | 120 | 53 | 35 | 110 | 87 | 1 | False |
Golem | 76 | Rock | Ground | 495 | 80 | 120 | 130 | 55 | 65 | 45 | 3 | False |
Kabutops | 141 | Rock | Water | 495 | 60 | 115 | 105 | 65 | 70 | 80 | 2 | False |
In [30]:
pok_att[['Attack']].describe()
Out[30]:
Attack | |
count | 10.000000 |
mean | 125.900000 |
std | 5.989806 |
min | 115.000000 |
25% | 121.250000 |
50% | 127.500000 |
75% | 130.000000 |
max | 134.000000 |
In [ ]: